diff --git a/analysis/matlab/derived/mergeA2_d0_10.csv b/analysis/matlab/derived/mergeA2_d0_10.csv
new file mode 100644
index 0000000..26a1889
--- /dev/null
+++ b/analysis/matlab/derived/mergeA2_d0_10.csv
@@ -0,0 +1,127 @@
+subject,group,day,success,total,day_c,day_c2
+Banh-mi-1,Electrode-Box-B2,0,13,84,-4.92063492063492,24.2126480221718
+Banh-mi-1,Electrode-Box-B2,1,35,86,-3.92063492063492,15.371378180902
+Banh-mi-1,Electrode-Box-B2,2,47,110,-2.92063492063492,8.53010833963215
+Banh-mi-1,Electrode-Box-B2,3,65,140,-1.92063492063492,3.68883849836231
+Banh-mi-1,Electrode-Box-B2,4,70,127,-0.920634920634921,0.847568657092467
+Banh-mi-1,Electrode-Box-B2,5,102,142,0.0793650793650791,0.0062988158226253
+Banh-mi-1,Electrode-Box-B2,6,90,131,1.07936507936508,1.16502897455278
+Banh-mi-1,Electrode-Box-B2,7,109,148,2.07936507936508,4.32375913328294
+Banh-mi-1,Electrode-Box-B2,8,104,137,3.07936507936508,9.4824892920131
+Banh-mi-1,Electrode-Box-B2,9,119,150,4.07936507936508,16.6412194507433
+Banh-mi-1,Electrode-Box-B2,10,121,158,5.07936507936508,25.7999496094734
+Banh-mi-2,Electrode-Box-A2,0,25,97,-4.92063492063492,24.2126480221718
+Banh-mi-2,Electrode-Box-A2,1,22,101,-3.92063492063492,15.371378180902
+Banh-mi-2,Electrode-Box-A2,2,22,119,-2.92063492063492,8.53010833963215
+Banh-mi-2,Electrode-Box-A2,3,29,118,-1.92063492063492,3.68883849836231
+Banh-mi-2,Electrode-Box-A2,4,27,136,-0.920634920634921,0.847568657092467
+Banh-mi-2,Electrode-Box-A2,5,43,146,0.0793650793650791,0.0062988158226253
+Banh-mi-2,Electrode-Box-A2,6,74,146,1.07936507936508,1.16502897455278
+Banh-mi-2,Electrode-Box-A2,7,70,148,2.07936507936508,4.32375913328294
+Banh-mi-2,Electrode-Box-A2,8,65,130,3.07936507936508,9.4824892920131
+Banh-mi-2,Electrode-Box-A2,9,79,151,4.07936507936508,16.6412194507433
+Banh-mi-2,Electrode-Box-A2,10,93,152,5.07936507936508,25.7999496094734
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+Egg-tart-1,Electrode-Box-B2,1,16,78,-3.92063492063492,15.371378180902
+Egg-tart-1,Electrode-Box-B2,2,23,103,-2.92063492063492,8.53010833963215
+Egg-tart-1,Electrode-Box-B2,3,63,120,-1.92063492063492,3.68883849836231
+Egg-tart-1,Electrode-Box-B2,4,69,132,-0.920634920634921,0.847568657092467
+Egg-tart-1,Electrode-Box-B2,5,83,136,0.0793650793650791,0.0062988158226253
+Egg-tart-1,Electrode-Box-B2,6,71,142,1.07936507936508,1.16502897455278
+Egg-tart-1,Electrode-Box-B2,7,79,138,2.07936507936508,4.32375913328294
+Egg-tart-1,Electrode-Box-B2,8,98,142,3.07936507936508,9.4824892920131
+Egg-tart-1,Electrode-Box-B2,9,89,139,4.07936507936508,16.6412194507433
+Egg-tart-1,Electrode-Box-B2,10,96,143,5.07936507936508,25.7999496094734
+Egg-tart-2,Electrode-Box-A2,0,9,32,-4.92063492063492,24.2126480221718
+Egg-tart-2,Electrode-Box-A2,1,2,38,-3.92063492063492,15.371378180902
+Egg-tart-2,Electrode-Box-A2,2,31,93,-2.92063492063492,8.53010833963215
+Egg-tart-2,Electrode-Box-A2,3,44,101,-1.92063492063492,3.68883849836231
+Egg-tart-2,Electrode-Box-A2,4,54,131,-0.920634920634921,0.847568657092467
+Egg-tart-2,Electrode-Box-A2,5,84,139,0.0793650793650791,0.0062988158226253
+Egg-tart-2,Electrode-Box-A2,6,85,145,1.07936507936508,1.16502897455278
+Egg-tart-2,Electrode-Box-A2,7,79,143,2.07936507936508,4.32375913328294
+Egg-tart-2,Electrode-Box-A2,8,76,131,3.07936507936508,9.4824892920131
+Egg-tart-2,Electrode-Box-A2,9,88,149,4.07936507936508,16.6412194507433
+Egg-tart-2,Electrode-Box-A2,10,81,151,5.07936507936508,25.7999496094734
+Khoai-lang-1,Electrode-Box-B2,0,3,69,-4.92063492063492,24.2126480221718
+Khoai-lang-1,Electrode-Box-B2,1,18,97,-3.92063492063492,15.371378180902
+Khoai-lang-1,Electrode-Box-B2,2,27,84,-2.92063492063492,8.53010833963215
+Khoai-lang-1,Electrode-Box-B2,3,46,121,-1.92063492063492,3.68883849836231
+Khoai-lang-1,Electrode-Box-B2,4,65,143,-0.920634920634921,0.847568657092467
+Khoai-lang-1,Electrode-Box-B2,5,76,141,0.0793650793650791,0.0062988158226253
+Khoai-lang-1,Electrode-Box-B2,6,79,146,1.07936507936508,1.16502897455278
+Khoai-lang-1,Electrode-Box-B2,7,81,153,2.07936507936508,4.32375913328294
+Khoai-lang-1,Electrode-Box-B2,8,98,144,3.07936507936508,9.4824892920131
+Khoai-lang-1,Electrode-Box-B2,9,101,149,4.07936507936508,16.6412194507433
+Khoai-lang-1,Electrode-Box-B2,10,103,150,5.07936507936508,25.7999496094734
+Khoai-lang-2,Naive,0,0,0,-4.92063492063492,24.2126480221718
+Khoai-lang-2,Naive,1,0,0,-3.92063492063492,15.371378180902
+Khoai-lang-2,Naive,2,10,47,-2.92063492063492,8.53010833963215
+Khoai-lang-2,Naive,3,11,52,-1.92063492063492,3.68883849836231
+Khoai-lang-2,Naive,4,9,56,-0.920634920634921,0.847568657092467
+Khoai-lang-2,Naive,5,34,95,0.0793650793650791,0.0062988158226253
+Khoai-lang-2,Naive,6,21,72,1.07936507936508,1.16502897455278
+Khoai-lang-2,Naive,7,23,99,2.07936507936508,4.32375913328294
+Khoai-lang-2,Naive,8,64,136,3.07936507936508,9.4824892920131
+Khoai-lang-2,Naive,9,75,131,4.07936507936508,16.6412194507433
+Khoai-lang-2,Naive,10,63,134,5.07936507936508,25.7999496094734
+Khoai-tay-1,Electrode-Box-A2,0,14,62,-4.92063492063492,24.2126480221718
+Khoai-tay-1,Electrode-Box-A2,1,22,83,-3.92063492063492,15.371378180902
+Khoai-tay-1,Electrode-Box-A2,2,6,79,-2.92063492063492,8.53010833963215
+Khoai-tay-1,Electrode-Box-A2,3,28,97,-1.92063492063492,3.68883849836231
+Khoai-tay-1,Electrode-Box-A2,4,60,134,-0.920634920634921,0.847568657092467
+Khoai-tay-1,Electrode-Box-A2,5,75,138,0.0793650793650791,0.0062988158226253
+Khoai-tay-1,Electrode-Box-A2,6,81,137,1.07936507936508,1.16502897455278
+Khoai-tay-1,Electrode-Box-A2,7,78,147,2.07936507936508,4.32375913328294
+Khoai-tay-1,Electrode-Box-A2,8,91,132,3.07936507936508,9.4824892920131
+Khoai-tay-1,Electrode-Box-A2,9,99,146,4.07936507936508,16.6412194507433
+Khoai-tay-1,Electrode-Box-A2,10,94,143,5.07936507936508,25.7999496094734
+Khoai-tay-2,Naive,1,21,68,-3.92063492063492,15.371378180902
+Khoai-tay-2,Naive,2,6,79,-2.92063492063492,8.53010833963215
+Khoai-tay-2,Naive,3,0,83,-1.92063492063492,3.68883849836231
+Khoai-tay-2,Naive,4,28,87,-0.920634920634921,0.847568657092467
+Khoai-tay-2,Naive,5,31,125,0.0793650793650791,0.0062988158226253
+Khoai-tay-2,Naive,6,62,144,1.07936507936508,1.16502897455278
+Khoai-tay-2,Naive,7,87,141,2.07936507936508,4.32375913328294
+Khoai-tay-2,Naive,8,105,152,3.07936507936508,9.4824892920131
+Khoai-tay-2,Naive,9,75,148,4.07936507936508,16.6412194507433
+Khoai-tay-2,Naive,10,101,149,5.07936507936508,25.7999496094734
+OM-2,Naive,0,1,7,-4.92063492063492,24.2126480221718
+OM-2,Naive,1,11,33,-3.92063492063492,15.371378180902
+OM-2,Naive,2,19,62,-2.92063492063492,8.53010833963215
+OM-2,Naive,3,29,117,-1.92063492063492,3.68883849836231
+OM-2,Naive,4,73,126,-0.920634920634921,0.847568657092467
+OM-2,Naive,5,53,135,0.0793650793650791,0.0062988158226253
+OM-2,Naive,6,73,138,1.07936507936508,1.16502897455278
+OM-2,Naive,7,80,131,2.07936507936508,4.32375913328294
+OM-2,Naive,8,91,141,3.07936507936508,9.4824892920131
+OM-2,Naive,9,90,135,4.07936507936508,16.6412194507433
+OM-2,Naive,10,95,142,5.07936507936508,25.7999496094734
+Root-beer-1,Electrode-Box-B2,0,11,85,-4.92063492063492,24.2126480221718
+Root-beer-1,Electrode-Box-B2,1,18,76,-3.92063492063492,15.371378180902
+Root-beer-1,Electrode-Box-B2,2,40,105,-2.92063492063492,8.53010833963215
+Root-beer-1,Electrode-Box-B2,3,55,134,-1.92063492063492,3.68883849836231
+Root-beer-1,Electrode-Box-B2,4,75,136,-0.920634920634921,0.847568657092467
+Root-beer-1,Electrode-Box-B2,5,64,133,0.0793650793650791,0.0062988158226253
+Root-beer-1,Electrode-Box-B2,6,104,139,1.07936507936508,1.16502897455278
+Root-beer-1,Electrode-Box-B2,7,98,148,2.07936507936508,4.32375913328294
+Root-beer-1,Electrode-Box-B2,8,81,145,3.07936507936508,9.4824892920131
+Root-beer-1,Electrode-Box-B2,9,89,156,4.07936507936508,16.6412194507433
+Root-beer-1,Electrode-Box-B2,10,105,158,5.07936507936508,25.7999496094734
+Root-beer-2,Electrode-Box-A2,0,22,74,-4.92063492063492,24.2126480221718
+Root-beer-2,Electrode-Box-A2,1,31,87,-3.92063492063492,15.371378180902
+Root-beer-2,Electrode-Box-A2,2,49,134,-2.92063492063492,8.53010833963215
+Root-beer-2,Electrode-Box-A2,3,31,89,-1.92063492063492,3.68883849836231
+Root-beer-2,Electrode-Box-A2,4,60,140,-0.920634920634921,0.847568657092467
+Root-beer-2,Electrode-Box-A2,5,84,147,0.0793650793650791,0.0062988158226253
+Vu-vuong,Naive,0,18,61,-4.92063492063492,24.2126480221718
+Vu-vuong,Naive,1,35,91,-3.92063492063492,15.371378180902
+Vu-vuong,Naive,2,61,127,-2.92063492063492,8.53010833963215
+Vu-vuong,Naive,3,34,146,-1.92063492063492,3.68883849836231
+Vu-vuong,Naive,4,61,141,-0.920634920634921,0.847568657092467
+Vu-vuong,Naive,5,37,151,0.0793650793650791,0.0062988158226253
+Vu-vuong,Naive,6,49,131,1.07936507936508,1.16502897455278
+Vu-vuong,Naive,7,65,149,2.07936507936508,4.32375913328294
+Vu-vuong,Naive,8,67,141,3.07936507936508,9.4824892920131
+Vu-vuong,Naive,9,73,140,4.07936507936508,16.6412194507433
+Vu-vuong,Naive,10,72,131,5.07936507936508,25.7999496094734
diff --git a/analysis/matlab/derived/mergeA2_d0_13.csv b/analysis/matlab/derived/mergeA2_d0_13.csv
new file mode 100644
index 0000000..d6ddd3d
--- /dev/null
+++ b/analysis/matlab/derived/mergeA2_d0_13.csv
@@ -0,0 +1,153 @@
+subject,group,day,success,total,day_c,day_c2
+Banh-mi-1,Electrode-Box-B2,0,13,84,-6.125,37.515625
+Banh-mi-1,Electrode-Box-B2,1,35,86,-5.125,26.265625
+Banh-mi-1,Electrode-Box-B2,2,47,110,-4.125,17.015625
+Banh-mi-1,Electrode-Box-B2,3,65,140,-3.125,9.765625
+Banh-mi-1,Electrode-Box-B2,4,70,127,-2.125,4.515625
+Banh-mi-1,Electrode-Box-B2,5,102,142,-1.125,1.265625
+Banh-mi-1,Electrode-Box-B2,6,90,131,-0.125,0.015625
+Banh-mi-1,Electrode-Box-B2,7,109,148,0.875,0.765625
+Banh-mi-1,Electrode-Box-B2,8,104,137,1.875,3.515625
+Banh-mi-1,Electrode-Box-B2,9,119,150,2.875,8.265625
+Banh-mi-1,Electrode-Box-B2,10,121,158,3.875,15.015625
+Banh-mi-1,Electrode-Box-B2,11,121,148,4.875,23.765625
+Banh-mi-1,Electrode-Box-B2,12,120,149,5.875,34.515625
+Banh-mi-1,Electrode-Box-B2,13,135,154,6.875,47.265625
+Banh-mi-2,Electrode-Box-A2,0,25,97,-6.125,37.515625
+Banh-mi-2,Electrode-Box-A2,1,22,101,-5.125,26.265625
+Banh-mi-2,Electrode-Box-A2,2,22,119,-4.125,17.015625
+Banh-mi-2,Electrode-Box-A2,3,29,118,-3.125,9.765625
+Banh-mi-2,Electrode-Box-A2,4,27,136,-2.125,4.515625
+Banh-mi-2,Electrode-Box-A2,5,43,146,-1.125,1.265625
+Banh-mi-2,Electrode-Box-A2,6,74,146,-0.125,0.015625
+Banh-mi-2,Electrode-Box-A2,7,70,148,0.875,0.765625
+Banh-mi-2,Electrode-Box-A2,8,65,130,1.875,3.515625
+Banh-mi-2,Electrode-Box-A2,9,79,151,2.875,8.265625
+Banh-mi-2,Electrode-Box-A2,10,93,152,3.875,15.015625
+Egg-tart-1,Electrode-Box-B2,0,7,56,-6.125,37.515625
+Egg-tart-1,Electrode-Box-B2,1,16,78,-5.125,26.265625
+Egg-tart-1,Electrode-Box-B2,2,23,103,-4.125,17.015625
+Egg-tart-1,Electrode-Box-B2,3,63,120,-3.125,9.765625
+Egg-tart-1,Electrode-Box-B2,4,69,132,-2.125,4.515625
+Egg-tart-1,Electrode-Box-B2,5,83,136,-1.125,1.265625
+Egg-tart-1,Electrode-Box-B2,6,71,142,-0.125,0.015625
+Egg-tart-1,Electrode-Box-B2,7,79,138,0.875,0.765625
+Egg-tart-1,Electrode-Box-B2,8,98,142,1.875,3.515625
+Egg-tart-1,Electrode-Box-B2,9,89,139,2.875,8.265625
+Egg-tart-1,Electrode-Box-B2,10,96,143,3.875,15.015625
+Egg-tart-1,Electrode-Box-B2,11,96,148,4.875,23.765625
+Egg-tart-1,Electrode-Box-B2,12,101,156,5.875,34.515625
+Egg-tart-1,Electrode-Box-B2,13,103,152,6.875,47.265625
+Egg-tart-2,Electrode-Box-A2,0,9,32,-6.125,37.515625
+Egg-tart-2,Electrode-Box-A2,1,2,38,-5.125,26.265625
+Egg-tart-2,Electrode-Box-A2,2,31,93,-4.125,17.015625
+Egg-tart-2,Electrode-Box-A2,3,44,101,-3.125,9.765625
+Egg-tart-2,Electrode-Box-A2,4,54,131,-2.125,4.515625
+Egg-tart-2,Electrode-Box-A2,5,84,139,-1.125,1.265625
+Egg-tart-2,Electrode-Box-A2,6,85,145,-0.125,0.015625
+Egg-tart-2,Electrode-Box-A2,7,79,143,0.875,0.765625
+Egg-tart-2,Electrode-Box-A2,8,76,131,1.875,3.515625
+Egg-tart-2,Electrode-Box-A2,9,88,149,2.875,8.265625
+Egg-tart-2,Electrode-Box-A2,10,81,151,3.875,15.015625
+Egg-tart-2,Electrode-Box-A2,11,78,152,4.875,23.765625
+Egg-tart-2,Electrode-Box-A2,12,96,155,5.875,34.515625
+Egg-tart-2,Electrode-Box-A2,13,84,155,6.875,47.265625
+Khoai-lang-1,Electrode-Box-B2,0,3,69,-6.125,37.515625
+Khoai-lang-1,Electrode-Box-B2,1,18,97,-5.125,26.265625
+Khoai-lang-1,Electrode-Box-B2,2,27,84,-4.125,17.015625
+Khoai-lang-1,Electrode-Box-B2,3,46,121,-3.125,9.765625
+Khoai-lang-1,Electrode-Box-B2,4,65,143,-2.125,4.515625
+Khoai-lang-1,Electrode-Box-B2,5,76,141,-1.125,1.265625
+Khoai-lang-1,Electrode-Box-B2,6,79,146,-0.125,0.015625
+Khoai-lang-1,Electrode-Box-B2,7,81,153,0.875,0.765625
+Khoai-lang-1,Electrode-Box-B2,8,98,144,1.875,3.515625
+Khoai-lang-1,Electrode-Box-B2,9,101,149,2.875,8.265625
+Khoai-lang-1,Electrode-Box-B2,10,103,150,3.875,15.015625
+Khoai-lang-1,Electrode-Box-B2,11,113,154,4.875,23.765625
+Khoai-lang-1,Electrode-Box-B2,12,117,148,5.875,34.515625
+Khoai-lang-1,Electrode-Box-B2,13,114,146,6.875,47.265625
+Khoai-lang-2,Naive,0,0,0,-6.125,37.515625
+Khoai-lang-2,Naive,1,0,0,-5.125,26.265625
+Khoai-lang-2,Naive,2,10,47,-4.125,17.015625
+Khoai-lang-2,Naive,3,11,52,-3.125,9.765625
+Khoai-lang-2,Naive,4,9,56,-2.125,4.515625
+Khoai-lang-2,Naive,5,34,95,-1.125,1.265625
+Khoai-lang-2,Naive,6,21,72,-0.125,0.015625
+Khoai-lang-2,Naive,7,23,99,0.875,0.765625
+Khoai-lang-2,Naive,8,64,136,1.875,3.515625
+Khoai-lang-2,Naive,9,75,131,2.875,8.265625
+Khoai-lang-2,Naive,10,63,134,3.875,15.015625
+Khoai-lang-2,Naive,11,59,139,4.875,23.765625
+Khoai-lang-2,Naive,12,51,129,5.875,34.515625
+Khoai-lang-2,Naive,13,73,143,6.875,47.265625
+Khoai-tay-1,Electrode-Box-A2,0,14,62,-6.125,37.515625
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+Vu-vuong,Naive,5,37,151,-1.125,1.265625
+Vu-vuong,Naive,6,49,131,-0.125,0.015625
+Vu-vuong,Naive,7,65,149,0.875,0.765625
+Vu-vuong,Naive,8,67,141,1.875,3.515625
+Vu-vuong,Naive,9,73,140,2.875,8.265625
+Vu-vuong,Naive,10,72,131,3.875,15.015625
+Vu-vuong,Naive,11,89,158,4.875,23.765625
+Vu-vuong,Naive,12,97,154,5.875,34.515625
+Vu-vuong,Naive,13,93,145,6.875,47.265625
diff --git a/analysis/matlab/derived/mergeA2_full.csv b/analysis/matlab/derived/mergeA2_full.csv
new file mode 100644
index 0000000..07d68db
--- /dev/null
+++ b/analysis/matlab/derived/mergeA2_full.csv
@@ -0,0 +1,186 @@
+subject,group,day,success,total,day_c,day_c2
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+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424
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+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532
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+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946
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+Vu-vuong,Naive,12,97,154,3.7027027027027,13.7100073046019
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+Vu-vuong,Naive,14,75,147,5.7027027027027,32.5208181154127
+Vu-vuong,Naive,15,68,142,6.7027027027027,44.9262235208181
+Vu-vuong,Naive,16,96,162,7.7027027027027,59.3316289262235
+Vu-vuong,Naive,17,68,144,8.7027027027027,75.7370343316289
+Vu-vuong,Naive,18,81,134,9.7027027027027,94.1424397370344
+Vu-vuong,Naive,19,66,144,10.7027027027027,114.54784514244
+Vu-vuong,Naive,20,84,127,11.7027027027027,136.953250547845
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diff --git a/analysis/matlab/derived/mergeB2_d0_10.csv b/analysis/matlab/derived/mergeB2_d0_10.csv
new file mode 100644
index 0000000..3559030
--- /dev/null
+++ b/analysis/matlab/derived/mergeB2_d0_10.csv
@@ -0,0 +1,127 @@
+subject,group,day,success,total,day_c,day_c2
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+Banh-mi-1,Electrode-Box-B2,2,47,110,-2.92063492063492,8.53010833963215
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+Banh-mi-1,Electrode-Box-B2,4,70,127,-0.920634920634921,0.847568657092467
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+Banh-mi-1,Electrode-Box-B2,6,90,131,1.07936507936508,1.16502897455278
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+Banh-mi-1,Electrode-Box-B2,8,104,137,3.07936507936508,9.4824892920131
+Banh-mi-1,Electrode-Box-B2,9,119,150,4.07936507936508,16.6412194507433
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+Banh-mi-2,Electrode-Box-A2,9,79,151,4.07936507936508,16.6412194507433
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diff --git a/analysis/matlab/derived/mergeB2_d0_13.csv b/analysis/matlab/derived/mergeB2_d0_13.csv
new file mode 100644
index 0000000..342f724
--- /dev/null
+++ b/analysis/matlab/derived/mergeB2_d0_13.csv
@@ -0,0 +1,153 @@
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+OM-2,Naive,10,95,142,3.875,15.015625
+OM-2,Naive,11,60,133,4.875,23.765625
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+Root-beer-1,Electrode-Box-B2,1,18,76,-5.125,26.265625
+Root-beer-1,Electrode-Box-B2,2,40,105,-4.125,17.015625
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+Root-beer-1,Electrode-Box-B2,9,89,156,2.875,8.265625
+Root-beer-1,Electrode-Box-B2,10,105,158,3.875,15.015625
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+Root-beer-2,Electrode-Box-A2,1,31,87,-5.125,26.265625
+Root-beer-2,Electrode-Box-A2,2,49,134,-4.125,17.015625
+Root-beer-2,Electrode-Box-A2,3,31,89,-3.125,9.765625
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+Root-beer-2,Electrode-Box-A2,5,84,147,-1.125,1.265625
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+Vu-vuong,Naive,2,61,127,-4.125,17.015625
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+Vu-vuong,Naive,4,61,141,-2.125,4.515625
+Vu-vuong,Naive,5,37,151,-1.125,1.265625
+Vu-vuong,Naive,6,49,131,-0.125,0.015625
+Vu-vuong,Naive,7,65,149,0.875,0.765625
+Vu-vuong,Naive,8,67,141,1.875,3.515625
+Vu-vuong,Naive,9,73,140,2.875,8.265625
+Vu-vuong,Naive,10,72,131,3.875,15.015625
+Vu-vuong,Naive,11,89,158,4.875,23.765625
+Vu-vuong,Naive,12,97,154,5.875,34.515625
+Vu-vuong,Naive,13,93,145,6.875,47.265625
diff --git a/analysis/matlab/derived/mergeB2_full.csv b/analysis/matlab/derived/mergeB2_full.csv
new file mode 100644
index 0000000..eceb064
--- /dev/null
+++ b/analysis/matlab/derived/mergeB2_full.csv
@@ -0,0 +1,186 @@
+subject,group,day,success,total,day_c,day_c2
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586
+Banh-mi-2,Electrode-Box-A2,5,43,146,-3.2972972972973,10.8721694667641
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946
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+Khoai-lang-2,Naive,23,88,139,14.7027027027027,216.169466764061
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+Khoai-tay-1,Electrode-Box-B2,9,99,146,0.702702702702704,0.493791088385684
+Khoai-tay-1,Electrode-Box-B2,10,94,143,1.7027027027027,2.89919649379109
+Khoai-tay-1,Electrode-Box-B2,11,110,156,2.7027027027027,7.3046018991965
+Khoai-tay-1,Electrode-Box-B2,12,105,143,3.7027027027027,13.7100073046019
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+Khoai-tay-1,Electrode-Box-B2,14,91,152,5.7027027027027,32.5208181154127
+Khoai-tay-2,Naive,1,21,68,-7.2972972972973,53.2505478451424
+Khoai-tay-2,Naive,2,6,79,-6.2972972972973,39.6559532505478
+Khoai-tay-2,Naive,3,0,83,-5.2972972972973,28.0613586559532
+Khoai-tay-2,Naive,4,28,87,-4.2972972972973,18.4667640613586
+Khoai-tay-2,Naive,5,31,125,-3.2972972972973,10.8721694667641
+Khoai-tay-2,Naive,6,62,144,-2.2972972972973,5.27757487216946
+Khoai-tay-2,Naive,7,87,141,-1.2972972972973,1.68298027757487
+Khoai-tay-2,Naive,8,105,152,-0.297297297297296,0.0883856829802771
+Khoai-tay-2,Naive,9,75,148,0.702702702702704,0.493791088385684
+Khoai-tay-2,Naive,10,101,149,1.7027027027027,2.89919649379109
+Khoai-tay-2,Naive,11,102,154,2.7027027027027,7.3046018991965
+Khoai-tay-2,Naive,12,95,144,3.7027027027027,13.7100073046019
+Khoai-tay-2,Naive,13,77,149,4.7027027027027,22.1154127100073
+Khoai-tay-2,Naive,14,91,153,5.7027027027027,32.5208181154127
+OM-2,Naive,0,1,7,-8.2972972972973,68.845142439737
+OM-2,Naive,1,11,33,-7.2972972972973,53.2505478451424
+OM-2,Naive,2,19,62,-6.2972972972973,39.6559532505478
+OM-2,Naive,3,29,117,-5.2972972972973,28.0613586559532
+OM-2,Naive,4,73,126,-4.2972972972973,18.4667640613586
+OM-2,Naive,5,53,135,-3.2972972972973,10.8721694667641
+OM-2,Naive,6,73,138,-2.2972972972973,5.27757487216946
+OM-2,Naive,7,80,131,-1.2972972972973,1.68298027757487
+OM-2,Naive,8,91,141,-0.297297297297296,0.0883856829802771
+OM-2,Naive,9,90,135,0.702702702702704,0.493791088385684
+OM-2,Naive,10,95,142,1.7027027027027,2.89919649379109
+OM-2,Naive,11,60,133,2.7027027027027,7.3046018991965
+OM-2,Naive,12,58,142,3.7027027027027,13.7100073046019
+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109
+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737
+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586
+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641
+Vu-vuong,Naive,0,18,61,-8.2972972972973,68.845142439737
+Vu-vuong,Naive,1,35,91,-7.2972972972973,53.2505478451424
+Vu-vuong,Naive,2,61,127,-6.2972972972973,39.6559532505478
+Vu-vuong,Naive,3,34,146,-5.2972972972973,28.0613586559532
+Vu-vuong,Naive,4,61,141,-4.2972972972973,18.4667640613586
+Vu-vuong,Naive,5,37,151,-3.2972972972973,10.8721694667641
+Vu-vuong,Naive,6,49,131,-2.2972972972973,5.27757487216946
+Vu-vuong,Naive,7,65,149,-1.2972972972973,1.68298027757487
+Vu-vuong,Naive,8,67,141,-0.297297297297296,0.0883856829802771
+Vu-vuong,Naive,9,73,140,0.702702702702704,0.493791088385684
+Vu-vuong,Naive,10,72,131,1.7027027027027,2.89919649379109
+Vu-vuong,Naive,11,89,158,2.7027027027027,7.3046018991965
+Vu-vuong,Naive,12,97,154,3.7027027027027,13.7100073046019
+Vu-vuong,Naive,13,93,145,4.7027027027027,22.1154127100073
+Vu-vuong,Naive,14,75,147,5.7027027027027,32.5208181154127
+Vu-vuong,Naive,15,68,142,6.7027027027027,44.9262235208181
+Vu-vuong,Naive,16,96,162,7.7027027027027,59.3316289262235
+Vu-vuong,Naive,17,68,144,8.7027027027027,75.7370343316289
+Vu-vuong,Naive,18,81,134,9.7027027027027,94.1424397370344
+Vu-vuong,Naive,19,66,144,10.7027027027027,114.54784514244
+Vu-vuong,Naive,20,84,127,11.7027027027027,136.953250547845
+Vu-vuong,Naive,21,66,133,12.7027027027027,161.358655953251
diff --git a/analysis/matlab/derived/mergeNaive_full.csv b/analysis/matlab/derived/mergeNaive_full.csv
new file mode 100644
index 0000000..8db6919
--- /dev/null
+++ b/analysis/matlab/derived/mergeNaive_full.csv
@@ -0,0 +1,186 @@
+subject,group,day,success,total,day_c,day_c2
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586
+Banh-mi-2,Electrode-Box-A2,5,43,146,-3.2972972972973,10.8721694667641
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109
+Egg-tart-1,Electrode-Box-B2,0,7,56,-8.2972972972973,68.845142439737
+Egg-tart-1,Electrode-Box-B2,1,16,78,-7.2972972972973,53.2505478451424
+Egg-tart-1,Electrode-Box-B2,2,23,103,-6.2972972972973,39.6559532505478
+Egg-tart-1,Electrode-Box-B2,3,63,120,-5.2972972972973,28.0613586559532
+Egg-tart-1,Electrode-Box-B2,4,69,132,-4.2972972972973,18.4667640613586
+Egg-tart-1,Electrode-Box-B2,5,83,136,-3.2972972972973,10.8721694667641
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109
+Egg-tart-1,Electrode-Box-B2,11,96,148,2.7027027027027,7.3046018991965
+Egg-tart-1,Electrode-Box-B2,12,101,156,3.7027027027027,13.7100073046019
+Egg-tart-1,Electrode-Box-B2,13,103,152,4.7027027027027,22.1154127100073
+Egg-tart-1,Electrode-Box-B2,14,97,152,5.7027027027027,32.5208181154127
+Egg-tart-2,Electrode-Box-A2,0,9,32,-8.2972972972973,68.845142439737
+Egg-tart-2,Electrode-Box-A2,1,2,38,-7.2972972972973,53.2505478451424
+Egg-tart-2,Electrode-Box-A2,2,31,93,-6.2972972972973,39.6559532505478
+Egg-tart-2,Electrode-Box-A2,3,44,101,-5.2972972972973,28.0613586559532
+Egg-tart-2,Electrode-Box-A2,4,54,131,-4.2972972972973,18.4667640613586
+Egg-tart-2,Electrode-Box-A2,5,84,139,-3.2972972972973,10.8721694667641
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771
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+Khoai-lang-1,Electrode-Box-B2,3,46,121,-5.2972972972973,28.0613586559532
+Khoai-lang-1,Electrode-Box-B2,4,65,143,-4.2972972972973,18.4667640613586
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+Khoai-lang-1,Electrode-Box-B2,11,113,154,2.7027027027027,7.3046018991965
+Khoai-lang-1,Electrode-Box-B2,12,117,148,3.7027027027027,13.7100073046019
+Khoai-lang-1,Electrode-Box-B2,13,114,146,4.7027027027027,22.1154127100073
+Khoai-lang-1,Electrode-Box-B2,14,119,132,5.7027027027027,32.5208181154127
+Khoai-lang-1,Electrode-Box-B2,15,111,156,6.7027027027027,44.9262235208181
+Khoai-lang-1,Electrode-Box-B2,16,74,156,7.7027027027027,59.3316289262235
+Khoai-lang-1,Electrode-Box-B2,17,109,139,8.7027027027027,75.7370343316289
+Khoai-lang-1,Electrode-Box-B2,18,88,138,9.7027027027027,94.1424397370344
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+Khoai-lang-2,Electrode-Box-A2,6,21,72,-2.2972972972973,5.27757487216946
+Khoai-lang-2,Electrode-Box-A2,7,23,99,-1.2972972972973,1.68298027757487
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+Khoai-lang-2,Electrode-Box-A2,9,75,131,0.702702702702704,0.493791088385684
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+Khoai-lang-2,Electrode-Box-A2,18,63,122,9.7027027027027,94.1424397370344
+Khoai-lang-2,Electrode-Box-A2,19,48,116,10.7027027027027,114.54784514244
+Khoai-lang-2,Electrode-Box-A2,20,65,134,11.7027027027027,136.953250547845
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+Khoai-lang-2,Electrode-Box-A2,22,98,146,13.7027027027027,187.764061358656
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+Khoai-lang-2,Electrode-Box-A2,24,94,148,15.7027027027027,246.574872169467
+Khoai-lang-2,Electrode-Box-A2,25,56,102,16.7027027027027,278.980277574872
+Khoai-lang-2,Electrode-Box-A2,26,75,143,17.7027027027027,313.385682980278
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+Khoai-tay-2,Electrode-Box-A2,4,28,87,-4.2972972972973,18.4667640613586
+Khoai-tay-2,Electrode-Box-A2,5,31,125,-3.2972972972973,10.8721694667641
+Khoai-tay-2,Electrode-Box-A2,6,62,144,-2.2972972972973,5.27757487216946
+Khoai-tay-2,Electrode-Box-A2,7,87,141,-1.2972972972973,1.68298027757487
+Khoai-tay-2,Electrode-Box-A2,8,105,152,-0.297297297297296,0.0883856829802771
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+OM-2,Electrode-Box-A2,1,11,33,-7.2972972972973,53.2505478451424
+OM-2,Electrode-Box-A2,2,19,62,-6.2972972972973,39.6559532505478
+OM-2,Electrode-Box-A2,3,29,117,-5.2972972972973,28.0613586559532
+OM-2,Electrode-Box-A2,4,73,126,-4.2972972972973,18.4667640613586
+OM-2,Electrode-Box-A2,5,53,135,-3.2972972972973,10.8721694667641
+OM-2,Electrode-Box-A2,6,73,138,-2.2972972972973,5.27757487216946
+OM-2,Electrode-Box-A2,7,80,131,-1.2972972972973,1.68298027757487
+OM-2,Electrode-Box-A2,8,91,141,-0.297297297297296,0.0883856829802771
+OM-2,Electrode-Box-A2,9,90,135,0.702702702702704,0.493791088385684
+OM-2,Electrode-Box-A2,10,95,142,1.7027027027027,2.89919649379109
+OM-2,Electrode-Box-A2,11,60,133,2.7027027027027,7.3046018991965
+OM-2,Electrode-Box-A2,12,58,142,3.7027027027027,13.7100073046019
+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109
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+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586
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+Vu-vuong,Electrode-Box-A2,3,34,146,-5.2972972972973,28.0613586559532
+Vu-vuong,Electrode-Box-A2,4,61,141,-4.2972972972973,18.4667640613586
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+Vu-vuong,Electrode-Box-A2,6,49,131,-2.2972972972973,5.27757487216946
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+Vu-vuong,Electrode-Box-A2,10,72,131,1.7027027027027,2.89919649379109
+Vu-vuong,Electrode-Box-A2,11,89,158,2.7027027027027,7.3046018991965
+Vu-vuong,Electrode-Box-A2,12,97,154,3.7027027027027,13.7100073046019
+Vu-vuong,Electrode-Box-A2,13,93,145,4.7027027027027,22.1154127100073
+Vu-vuong,Electrode-Box-A2,14,75,147,5.7027027027027,32.5208181154127
+Vu-vuong,Electrode-Box-A2,15,68,142,6.7027027027027,44.9262235208181
+Vu-vuong,Electrode-Box-A2,16,96,162,7.7027027027027,59.3316289262235
+Vu-vuong,Electrode-Box-A2,17,68,144,8.7027027027027,75.7370343316289
+Vu-vuong,Electrode-Box-A2,18,81,134,9.7027027027027,94.1424397370344
+Vu-vuong,Electrode-Box-A2,19,66,144,10.7027027027027,114.54784514244
+Vu-vuong,Electrode-Box-A2,20,84,127,11.7027027027027,136.953250547845
+Vu-vuong,Electrode-Box-A2,21,66,133,12.7027027027027,161.358655953251
diff --git a/analysis/matlab/derived/unmerged_d0_10.csv b/analysis/matlab/derived/unmerged_d0_10.csv
new file mode 100644
index 0000000..c581905
--- /dev/null
+++ b/analysis/matlab/derived/unmerged_d0_10.csv
@@ -0,0 +1,127 @@
+subject,group,day,success,total,day_c,day_c2
+Banh-mi-1,Electrode-Box-B2,0,13,84,-4.92063492063492,24.2126480221718
+Banh-mi-1,Electrode-Box-B2,1,35,86,-3.92063492063492,15.371378180902
+Banh-mi-1,Electrode-Box-B2,2,47,110,-2.92063492063492,8.53010833963215
+Banh-mi-1,Electrode-Box-B2,3,65,140,-1.92063492063492,3.68883849836231
+Banh-mi-1,Electrode-Box-B2,4,70,127,-0.920634920634921,0.847568657092467
+Banh-mi-1,Electrode-Box-B2,5,102,142,0.0793650793650791,0.0062988158226253
+Banh-mi-1,Electrode-Box-B2,6,90,131,1.07936507936508,1.16502897455278
+Banh-mi-1,Electrode-Box-B2,7,109,148,2.07936507936508,4.32375913328294
+Banh-mi-1,Electrode-Box-B2,8,104,137,3.07936507936508,9.4824892920131
+Banh-mi-1,Electrode-Box-B2,9,119,150,4.07936507936508,16.6412194507433
+Banh-mi-1,Electrode-Box-B2,10,121,158,5.07936507936508,25.7999496094734
+Banh-mi-2,Electrode-Box-A2,0,25,97,-4.92063492063492,24.2126480221718
+Banh-mi-2,Electrode-Box-A2,1,22,101,-3.92063492063492,15.371378180902
+Banh-mi-2,Electrode-Box-A2,2,22,119,-2.92063492063492,8.53010833963215
+Banh-mi-2,Electrode-Box-A2,3,29,118,-1.92063492063492,3.68883849836231
+Banh-mi-2,Electrode-Box-A2,4,27,136,-0.920634920634921,0.847568657092467
+Banh-mi-2,Electrode-Box-A2,5,43,146,0.0793650793650791,0.0062988158226253
+Banh-mi-2,Electrode-Box-A2,6,74,146,1.07936507936508,1.16502897455278
+Banh-mi-2,Electrode-Box-A2,7,70,148,2.07936507936508,4.32375913328294
+Banh-mi-2,Electrode-Box-A2,8,65,130,3.07936507936508,9.4824892920131
+Banh-mi-2,Electrode-Box-A2,9,79,151,4.07936507936508,16.6412194507433
+Banh-mi-2,Electrode-Box-A2,10,93,152,5.07936507936508,25.7999496094734
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+Egg-tart-1,Electrode-Box-B2,1,16,78,-3.92063492063492,15.371378180902
+Egg-tart-1,Electrode-Box-B2,2,23,103,-2.92063492063492,8.53010833963215
+Egg-tart-1,Electrode-Box-B2,3,63,120,-1.92063492063492,3.68883849836231
+Egg-tart-1,Electrode-Box-B2,4,69,132,-0.920634920634921,0.847568657092467
+Egg-tart-1,Electrode-Box-B2,5,83,136,0.0793650793650791,0.0062988158226253
+Egg-tart-1,Electrode-Box-B2,6,71,142,1.07936507936508,1.16502897455278
+Egg-tart-1,Electrode-Box-B2,7,79,138,2.07936507936508,4.32375913328294
+Egg-tart-1,Electrode-Box-B2,8,98,142,3.07936507936508,9.4824892920131
+Egg-tart-1,Electrode-Box-B2,9,89,139,4.07936507936508,16.6412194507433
+Egg-tart-1,Electrode-Box-B2,10,96,143,5.07936507936508,25.7999496094734
+Egg-tart-2,Electrode-Box-A2,0,9,32,-4.92063492063492,24.2126480221718
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+Egg-tart-2,Electrode-Box-A2,2,31,93,-2.92063492063492,8.53010833963215
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+Egg-tart-2,Electrode-Box-A2,4,54,131,-0.920634920634921,0.847568657092467
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+Egg-tart-2,Electrode-Box-A2,6,85,145,1.07936507936508,1.16502897455278
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+Egg-tart-2,Electrode-Box-A2,8,76,131,3.07936507936508,9.4824892920131
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+Khoai-lang-1,Right-Electrode,1,18,97,-3.92063492063492,15.371378180902
+Khoai-lang-1,Right-Electrode,2,27,84,-2.92063492063492,8.53010833963215
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+Khoai-lang-1,Right-Electrode,4,65,143,-0.920634920634921,0.847568657092467
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+Khoai-lang-1,Right-Electrode,9,101,149,4.07936507936508,16.6412194507433
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+Khoai-tay-2,Naive,6,62,144,1.07936507936508,1.16502897455278
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+Khoai-tay-2,Naive,8,105,152,3.07936507936508,9.4824892920131
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+OM-2,Naive,7,80,131,2.07936507936508,4.32375913328294
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diff --git a/analysis/matlab/derived/unmerged_d0_13.csv b/analysis/matlab/derived/unmerged_d0_13.csv
new file mode 100644
index 0000000..6f92936
--- /dev/null
+++ b/analysis/matlab/derived/unmerged_d0_13.csv
@@ -0,0 +1,153 @@
+subject,group,day,success,total,day_c,day_c2
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+Banh-mi-1,Electrode-Box-B2,1,35,86,-5.125,26.265625
+Banh-mi-1,Electrode-Box-B2,2,47,110,-4.125,17.015625
+Banh-mi-1,Electrode-Box-B2,3,65,140,-3.125,9.765625
+Banh-mi-1,Electrode-Box-B2,4,70,127,-2.125,4.515625
+Banh-mi-1,Electrode-Box-B2,5,102,142,-1.125,1.265625
+Banh-mi-1,Electrode-Box-B2,6,90,131,-0.125,0.015625
+Banh-mi-1,Electrode-Box-B2,7,109,148,0.875,0.765625
+Banh-mi-1,Electrode-Box-B2,8,104,137,1.875,3.515625
+Banh-mi-1,Electrode-Box-B2,9,119,150,2.875,8.265625
+Banh-mi-1,Electrode-Box-B2,10,121,158,3.875,15.015625
+Banh-mi-1,Electrode-Box-B2,11,121,148,4.875,23.765625
+Banh-mi-1,Electrode-Box-B2,12,120,149,5.875,34.515625
+Banh-mi-1,Electrode-Box-B2,13,135,154,6.875,47.265625
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+Banh-mi-2,Electrode-Box-A2,1,22,101,-5.125,26.265625
+Banh-mi-2,Electrode-Box-A2,2,22,119,-4.125,17.015625
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+Egg-tart-1,Electrode-Box-B2,2,23,103,-4.125,17.015625
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+Khoai-lang-1,Right-Electrode,2,27,84,-4.125,17.015625
+Khoai-lang-1,Right-Electrode,3,46,121,-3.125,9.765625
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+Khoai-lang-1,Right-Electrode,9,101,149,2.875,8.265625
+Khoai-lang-1,Right-Electrode,10,103,150,3.875,15.015625
+Khoai-lang-1,Right-Electrode,11,113,154,4.875,23.765625
+Khoai-lang-1,Right-Electrode,12,117,148,5.875,34.515625
+Khoai-lang-1,Right-Electrode,13,114,146,6.875,47.265625
+Khoai-lang-2,Naive,0,0,0,-6.125,37.515625
+Khoai-lang-2,Naive,1,0,0,-5.125,26.265625
+Khoai-lang-2,Naive,2,10,47,-4.125,17.015625
+Khoai-lang-2,Naive,3,11,52,-3.125,9.765625
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+Khoai-lang-2,Naive,5,34,95,-1.125,1.265625
+Khoai-lang-2,Naive,6,21,72,-0.125,0.015625
+Khoai-lang-2,Naive,7,23,99,0.875,0.765625
+Khoai-lang-2,Naive,8,64,136,1.875,3.515625
+Khoai-lang-2,Naive,9,75,131,2.875,8.265625
+Khoai-lang-2,Naive,10,63,134,3.875,15.015625
+Khoai-lang-2,Naive,11,59,139,4.875,23.765625
+Khoai-lang-2,Naive,12,51,129,5.875,34.515625
+Khoai-lang-2,Naive,13,73,143,6.875,47.265625
+Khoai-tay-1,Electrode-Box-A,0,14,62,-6.125,37.515625
+Khoai-tay-1,Electrode-Box-A,1,22,83,-5.125,26.265625
+Khoai-tay-1,Electrode-Box-A,2,6,79,-4.125,17.015625
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+Khoai-tay-1,Electrode-Box-A,4,60,134,-2.125,4.515625
+Khoai-tay-1,Electrode-Box-A,5,75,138,-1.125,1.265625
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+Khoai-tay-1,Electrode-Box-A,8,91,132,1.875,3.515625
+Khoai-tay-1,Electrode-Box-A,9,99,146,2.875,8.265625
+Khoai-tay-1,Electrode-Box-A,10,94,143,3.875,15.015625
+Khoai-tay-1,Electrode-Box-A,11,110,156,4.875,23.765625
+Khoai-tay-1,Electrode-Box-A,12,105,143,5.875,34.515625
+Khoai-tay-1,Electrode-Box-A,13,106,153,6.875,47.265625
+Khoai-tay-2,Naive,1,21,68,-5.125,26.265625
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+Khoai-tay-2,Naive,3,0,83,-3.125,9.765625
+Khoai-tay-2,Naive,4,28,87,-2.125,4.515625
+Khoai-tay-2,Naive,5,31,125,-1.125,1.265625
+Khoai-tay-2,Naive,6,62,144,-0.125,0.015625
+Khoai-tay-2,Naive,7,87,141,0.875,0.765625
+Khoai-tay-2,Naive,8,105,152,1.875,3.515625
+Khoai-tay-2,Naive,9,75,148,2.875,8.265625
+Khoai-tay-2,Naive,10,101,149,3.875,15.015625
+Khoai-tay-2,Naive,11,102,154,4.875,23.765625
+Khoai-tay-2,Naive,12,95,144,5.875,34.515625
+Khoai-tay-2,Naive,13,77,149,6.875,47.265625
+OM-2,Naive,0,1,7,-6.125,37.515625
+OM-2,Naive,1,11,33,-5.125,26.265625
+OM-2,Naive,2,19,62,-4.125,17.015625
+OM-2,Naive,3,29,117,-3.125,9.765625
+OM-2,Naive,4,73,126,-2.125,4.515625
+OM-2,Naive,5,53,135,-1.125,1.265625
+OM-2,Naive,6,73,138,-0.125,0.015625
+OM-2,Naive,7,80,131,0.875,0.765625
+OM-2,Naive,8,91,141,1.875,3.515625
+OM-2,Naive,9,90,135,2.875,8.265625
+OM-2,Naive,10,95,142,3.875,15.015625
+OM-2,Naive,11,60,133,4.875,23.765625
+OM-2,Naive,12,58,142,5.875,34.515625
+Root-beer-1,Electrode-Box-B2,0,11,85,-6.125,37.515625
+Root-beer-1,Electrode-Box-B2,1,18,76,-5.125,26.265625
+Root-beer-1,Electrode-Box-B2,2,40,105,-4.125,17.015625
+Root-beer-1,Electrode-Box-B2,3,55,134,-3.125,9.765625
+Root-beer-1,Electrode-Box-B2,4,75,136,-2.125,4.515625
+Root-beer-1,Electrode-Box-B2,5,64,133,-1.125,1.265625
+Root-beer-1,Electrode-Box-B2,6,104,139,-0.125,0.015625
+Root-beer-1,Electrode-Box-B2,7,98,148,0.875,0.765625
+Root-beer-1,Electrode-Box-B2,8,81,145,1.875,3.515625
+Root-beer-1,Electrode-Box-B2,9,89,156,2.875,8.265625
+Root-beer-1,Electrode-Box-B2,10,105,158,3.875,15.015625
+Root-beer-2,Electrode-Box-A2,0,22,74,-6.125,37.515625
+Root-beer-2,Electrode-Box-A2,1,31,87,-5.125,26.265625
+Root-beer-2,Electrode-Box-A2,2,49,134,-4.125,17.015625
+Root-beer-2,Electrode-Box-A2,3,31,89,-3.125,9.765625
+Root-beer-2,Electrode-Box-A2,4,60,140,-2.125,4.515625
+Root-beer-2,Electrode-Box-A2,5,84,147,-1.125,1.265625
+Vu-vuong,Naive,0,18,61,-6.125,37.515625
+Vu-vuong,Naive,1,35,91,-5.125,26.265625
+Vu-vuong,Naive,2,61,127,-4.125,17.015625
+Vu-vuong,Naive,3,34,146,-3.125,9.765625
+Vu-vuong,Naive,4,61,141,-2.125,4.515625
+Vu-vuong,Naive,5,37,151,-1.125,1.265625
+Vu-vuong,Naive,6,49,131,-0.125,0.015625
+Vu-vuong,Naive,7,65,149,0.875,0.765625
+Vu-vuong,Naive,8,67,141,1.875,3.515625
+Vu-vuong,Naive,9,73,140,2.875,8.265625
+Vu-vuong,Naive,10,72,131,3.875,15.015625
+Vu-vuong,Naive,11,89,158,4.875,23.765625
+Vu-vuong,Naive,12,97,154,5.875,34.515625
+Vu-vuong,Naive,13,93,145,6.875,47.265625
diff --git a/analysis/matlab/derived/unmerged_full.csv b/analysis/matlab/derived/unmerged_full.csv
new file mode 100644
index 0000000..c4dd920
--- /dev/null
+++ b/analysis/matlab/derived/unmerged_full.csv
@@ -0,0 +1,186 @@
+subject,group,day,success,total,day_c,day_c2
+Banh-mi-1,Electrode-Box-B2,0,13,84,-8.2972972972973,68.845142439737
+Banh-mi-1,Electrode-Box-B2,1,35,86,-7.2972972972973,53.2505478451424
+Banh-mi-1,Electrode-Box-B2,2,47,110,-6.2972972972973,39.6559532505478
+Banh-mi-1,Electrode-Box-B2,3,65,140,-5.2972972972973,28.0613586559532
+Banh-mi-1,Electrode-Box-B2,4,70,127,-4.2972972972973,18.4667640613586
+Banh-mi-1,Electrode-Box-B2,5,102,142,-3.2972972972973,10.8721694667641
+Banh-mi-1,Electrode-Box-B2,6,90,131,-2.2972972972973,5.27757487216946
+Banh-mi-1,Electrode-Box-B2,7,109,148,-1.2972972972973,1.68298027757487
+Banh-mi-1,Electrode-Box-B2,8,104,137,-0.297297297297296,0.0883856829802771
+Banh-mi-1,Electrode-Box-B2,9,119,150,0.702702702702704,0.493791088385684
+Banh-mi-1,Electrode-Box-B2,10,121,158,1.7027027027027,2.89919649379109
+Banh-mi-1,Electrode-Box-B2,11,121,148,2.7027027027027,7.3046018991965
+Banh-mi-1,Electrode-Box-B2,12,120,149,3.7027027027027,13.7100073046019
+Banh-mi-1,Electrode-Box-B2,13,135,154,4.7027027027027,22.1154127100073
+Banh-mi-2,Electrode-Box-A2,0,25,97,-8.2972972972973,68.845142439737
+Banh-mi-2,Electrode-Box-A2,1,22,101,-7.2972972972973,53.2505478451424
+Banh-mi-2,Electrode-Box-A2,2,22,119,-6.2972972972973,39.6559532505478
+Banh-mi-2,Electrode-Box-A2,3,29,118,-5.2972972972973,28.0613586559532
+Banh-mi-2,Electrode-Box-A2,4,27,136,-4.2972972972973,18.4667640613586
+Banh-mi-2,Electrode-Box-A2,5,43,146,-3.2972972972973,10.8721694667641
+Banh-mi-2,Electrode-Box-A2,6,74,146,-2.2972972972973,5.27757487216946
+Banh-mi-2,Electrode-Box-A2,7,70,148,-1.2972972972973,1.68298027757487
+Banh-mi-2,Electrode-Box-A2,8,65,130,-0.297297297297296,0.0883856829802771
+Banh-mi-2,Electrode-Box-A2,9,79,151,0.702702702702704,0.493791088385684
+Banh-mi-2,Electrode-Box-A2,10,93,152,1.7027027027027,2.89919649379109
+Egg-tart-1,Electrode-Box-B2,0,7,56,-8.2972972972973,68.845142439737
+Egg-tart-1,Electrode-Box-B2,1,16,78,-7.2972972972973,53.2505478451424
+Egg-tart-1,Electrode-Box-B2,2,23,103,-6.2972972972973,39.6559532505478
+Egg-tart-1,Electrode-Box-B2,3,63,120,-5.2972972972973,28.0613586559532
+Egg-tart-1,Electrode-Box-B2,4,69,132,-4.2972972972973,18.4667640613586
+Egg-tart-1,Electrode-Box-B2,5,83,136,-3.2972972972973,10.8721694667641
+Egg-tart-1,Electrode-Box-B2,6,71,142,-2.2972972972973,5.27757487216946
+Egg-tart-1,Electrode-Box-B2,7,79,138,-1.2972972972973,1.68298027757487
+Egg-tart-1,Electrode-Box-B2,8,98,142,-0.297297297297296,0.0883856829802771
+Egg-tart-1,Electrode-Box-B2,9,89,139,0.702702702702704,0.493791088385684
+Egg-tart-1,Electrode-Box-B2,10,96,143,1.7027027027027,2.89919649379109
+Egg-tart-1,Electrode-Box-B2,11,96,148,2.7027027027027,7.3046018991965
+Egg-tart-1,Electrode-Box-B2,12,101,156,3.7027027027027,13.7100073046019
+Egg-tart-1,Electrode-Box-B2,13,103,152,4.7027027027027,22.1154127100073
+Egg-tart-1,Electrode-Box-B2,14,97,152,5.7027027027027,32.5208181154127
+Egg-tart-2,Electrode-Box-A2,0,9,32,-8.2972972972973,68.845142439737
+Egg-tart-2,Electrode-Box-A2,1,2,38,-7.2972972972973,53.2505478451424
+Egg-tart-2,Electrode-Box-A2,2,31,93,-6.2972972972973,39.6559532505478
+Egg-tart-2,Electrode-Box-A2,3,44,101,-5.2972972972973,28.0613586559532
+Egg-tart-2,Electrode-Box-A2,4,54,131,-4.2972972972973,18.4667640613586
+Egg-tart-2,Electrode-Box-A2,5,84,139,-3.2972972972973,10.8721694667641
+Egg-tart-2,Electrode-Box-A2,6,85,145,-2.2972972972973,5.27757487216946
+Egg-tart-2,Electrode-Box-A2,7,79,143,-1.2972972972973,1.68298027757487
+Egg-tart-2,Electrode-Box-A2,8,76,131,-0.297297297297296,0.0883856829802771
+Egg-tart-2,Electrode-Box-A2,9,88,149,0.702702702702704,0.493791088385684
+Egg-tart-2,Electrode-Box-A2,10,81,151,1.7027027027027,2.89919649379109
+Egg-tart-2,Electrode-Box-A2,11,78,152,2.7027027027027,7.3046018991965
+Egg-tart-2,Electrode-Box-A2,12,96,155,3.7027027027027,13.7100073046019
+Egg-tart-2,Electrode-Box-A2,13,84,155,4.7027027027027,22.1154127100073
+Khoai-lang-1,Right-Electrode,0,3,69,-8.2972972972973,68.845142439737
+Khoai-lang-1,Right-Electrode,1,18,97,-7.2972972972973,53.2505478451424
+Khoai-lang-1,Right-Electrode,2,27,84,-6.2972972972973,39.6559532505478
+Khoai-lang-1,Right-Electrode,3,46,121,-5.2972972972973,28.0613586559532
+Khoai-lang-1,Right-Electrode,4,65,143,-4.2972972972973,18.4667640613586
+Khoai-lang-1,Right-Electrode,5,76,141,-3.2972972972973,10.8721694667641
+Khoai-lang-1,Right-Electrode,6,79,146,-2.2972972972973,5.27757487216946
+Khoai-lang-1,Right-Electrode,7,81,153,-1.2972972972973,1.68298027757487
+Khoai-lang-1,Right-Electrode,8,98,144,-0.297297297297296,0.0883856829802771
+Khoai-lang-1,Right-Electrode,9,101,149,0.702702702702704,0.493791088385684
+Khoai-lang-1,Right-Electrode,10,103,150,1.7027027027027,2.89919649379109
+Khoai-lang-1,Right-Electrode,11,113,154,2.7027027027027,7.3046018991965
+Khoai-lang-1,Right-Electrode,12,117,148,3.7027027027027,13.7100073046019
+Khoai-lang-1,Right-Electrode,13,114,146,4.7027027027027,22.1154127100073
+Khoai-lang-1,Right-Electrode,14,119,132,5.7027027027027,32.5208181154127
+Khoai-lang-1,Right-Electrode,15,111,156,6.7027027027027,44.9262235208181
+Khoai-lang-1,Right-Electrode,16,74,156,7.7027027027027,59.3316289262235
+Khoai-lang-1,Right-Electrode,17,109,139,8.7027027027027,75.7370343316289
+Khoai-lang-1,Right-Electrode,18,88,138,9.7027027027027,94.1424397370344
+Khoai-lang-1,Right-Electrode,19,98,140,10.7027027027027,114.54784514244
+Khoai-lang-1,Right-Electrode,20,118,153,11.7027027027027,136.953250547845
+Khoai-lang-1,Right-Electrode,21,109,146,12.7027027027027,161.358655953251
+Khoai-lang-1,Right-Electrode,22,106,141,13.7027027027027,187.764061358656
+Khoai-lang-2,Naive,0,0,0,-8.2972972972973,68.845142439737
+Khoai-lang-2,Naive,1,0,0,-7.2972972972973,53.2505478451424
+Khoai-lang-2,Naive,2,10,47,-6.2972972972973,39.6559532505478
+Khoai-lang-2,Naive,3,11,52,-5.2972972972973,28.0613586559532
+Khoai-lang-2,Naive,4,9,56,-4.2972972972973,18.4667640613586
+Khoai-lang-2,Naive,5,34,95,-3.2972972972973,10.8721694667641
+Khoai-lang-2,Naive,6,21,72,-2.2972972972973,5.27757487216946
+Khoai-lang-2,Naive,7,23,99,-1.2972972972973,1.68298027757487
+Khoai-lang-2,Naive,8,64,136,-0.297297297297296,0.0883856829802771
+Khoai-lang-2,Naive,9,75,131,0.702702702702704,0.493791088385684
+Khoai-lang-2,Naive,10,63,134,1.7027027027027,2.89919649379109
+Khoai-lang-2,Naive,11,59,139,2.7027027027027,7.3046018991965
+Khoai-lang-2,Naive,12,51,129,3.7027027027027,13.7100073046019
+Khoai-lang-2,Naive,13,73,143,4.7027027027027,22.1154127100073
+Khoai-lang-2,Naive,14,82,136,5.7027027027027,32.5208181154127
+Khoai-lang-2,Naive,15,70,145,6.7027027027027,44.9262235208181
+Khoai-lang-2,Naive,16,76,135,7.7027027027027,59.3316289262235
+Khoai-lang-2,Naive,17,76,150,8.7027027027027,75.7370343316289
+Khoai-lang-2,Naive,18,63,122,9.7027027027027,94.1424397370344
+Khoai-lang-2,Naive,19,48,116,10.7027027027027,114.54784514244
+Khoai-lang-2,Naive,20,65,134,11.7027027027027,136.953250547845
+Khoai-lang-2,Naive,21,75,131,12.7027027027027,161.358655953251
+Khoai-lang-2,Naive,22,98,146,13.7027027027027,187.764061358656
+Khoai-lang-2,Naive,23,88,139,14.7027027027027,216.169466764061
+Khoai-lang-2,Naive,24,94,148,15.7027027027027,246.574872169467
+Khoai-lang-2,Naive,25,56,102,16.7027027027027,278.980277574872
+Khoai-lang-2,Naive,26,75,143,17.7027027027027,313.385682980278
+Khoai-tay-1,Electrode-Box-A,0,14,62,-8.2972972972973,68.845142439737
+Khoai-tay-1,Electrode-Box-A,1,22,83,-7.2972972972973,53.2505478451424
+Khoai-tay-1,Electrode-Box-A,2,6,79,-6.2972972972973,39.6559532505478
+Khoai-tay-1,Electrode-Box-A,3,28,97,-5.2972972972973,28.0613586559532
+Khoai-tay-1,Electrode-Box-A,4,60,134,-4.2972972972973,18.4667640613586
+Khoai-tay-1,Electrode-Box-A,5,75,138,-3.2972972972973,10.8721694667641
+Khoai-tay-1,Electrode-Box-A,6,81,137,-2.2972972972973,5.27757487216946
+Khoai-tay-1,Electrode-Box-A,7,78,147,-1.2972972972973,1.68298027757487
+Khoai-tay-1,Electrode-Box-A,8,91,132,-0.297297297297296,0.0883856829802771
+Khoai-tay-1,Electrode-Box-A,9,99,146,0.702702702702704,0.493791088385684
+Khoai-tay-1,Electrode-Box-A,10,94,143,1.7027027027027,2.89919649379109
+Khoai-tay-1,Electrode-Box-A,11,110,156,2.7027027027027,7.3046018991965
+Khoai-tay-1,Electrode-Box-A,12,105,143,3.7027027027027,13.7100073046019
+Khoai-tay-1,Electrode-Box-A,13,106,153,4.7027027027027,22.1154127100073
+Khoai-tay-1,Electrode-Box-A,14,91,152,5.7027027027027,32.5208181154127
+Khoai-tay-2,Naive,1,21,68,-7.2972972972973,53.2505478451424
+Khoai-tay-2,Naive,2,6,79,-6.2972972972973,39.6559532505478
+Khoai-tay-2,Naive,3,0,83,-5.2972972972973,28.0613586559532
+Khoai-tay-2,Naive,4,28,87,-4.2972972972973,18.4667640613586
+Khoai-tay-2,Naive,5,31,125,-3.2972972972973,10.8721694667641
+Khoai-tay-2,Naive,6,62,144,-2.2972972972973,5.27757487216946
+Khoai-tay-2,Naive,7,87,141,-1.2972972972973,1.68298027757487
+Khoai-tay-2,Naive,8,105,152,-0.297297297297296,0.0883856829802771
+Khoai-tay-2,Naive,9,75,148,0.702702702702704,0.493791088385684
+Khoai-tay-2,Naive,10,101,149,1.7027027027027,2.89919649379109
+Khoai-tay-2,Naive,11,102,154,2.7027027027027,7.3046018991965
+Khoai-tay-2,Naive,12,95,144,3.7027027027027,13.7100073046019
+Khoai-tay-2,Naive,13,77,149,4.7027027027027,22.1154127100073
+Khoai-tay-2,Naive,14,91,153,5.7027027027027,32.5208181154127
+OM-2,Naive,0,1,7,-8.2972972972973,68.845142439737
+OM-2,Naive,1,11,33,-7.2972972972973,53.2505478451424
+OM-2,Naive,2,19,62,-6.2972972972973,39.6559532505478
+OM-2,Naive,3,29,117,-5.2972972972973,28.0613586559532
+OM-2,Naive,4,73,126,-4.2972972972973,18.4667640613586
+OM-2,Naive,5,53,135,-3.2972972972973,10.8721694667641
+OM-2,Naive,6,73,138,-2.2972972972973,5.27757487216946
+OM-2,Naive,7,80,131,-1.2972972972973,1.68298027757487
+OM-2,Naive,8,91,141,-0.297297297297296,0.0883856829802771
+OM-2,Naive,9,90,135,0.702702702702704,0.493791088385684
+OM-2,Naive,10,95,142,1.7027027027027,2.89919649379109
+OM-2,Naive,11,60,133,2.7027027027027,7.3046018991965
+OM-2,Naive,12,58,142,3.7027027027027,13.7100073046019
+Root-beer-1,Electrode-Box-B2,0,11,85,-8.2972972972973,68.845142439737
+Root-beer-1,Electrode-Box-B2,1,18,76,-7.2972972972973,53.2505478451424
+Root-beer-1,Electrode-Box-B2,2,40,105,-6.2972972972973,39.6559532505478
+Root-beer-1,Electrode-Box-B2,3,55,134,-5.2972972972973,28.0613586559532
+Root-beer-1,Electrode-Box-B2,4,75,136,-4.2972972972973,18.4667640613586
+Root-beer-1,Electrode-Box-B2,5,64,133,-3.2972972972973,10.8721694667641
+Root-beer-1,Electrode-Box-B2,6,104,139,-2.2972972972973,5.27757487216946
+Root-beer-1,Electrode-Box-B2,7,98,148,-1.2972972972973,1.68298027757487
+Root-beer-1,Electrode-Box-B2,8,81,145,-0.297297297297296,0.0883856829802771
+Root-beer-1,Electrode-Box-B2,9,89,156,0.702702702702704,0.493791088385684
+Root-beer-1,Electrode-Box-B2,10,105,158,1.7027027027027,2.89919649379109
+Root-beer-2,Electrode-Box-A2,0,22,74,-8.2972972972973,68.845142439737
+Root-beer-2,Electrode-Box-A2,1,31,87,-7.2972972972973,53.2505478451424
+Root-beer-2,Electrode-Box-A2,2,49,134,-6.2972972972973,39.6559532505478
+Root-beer-2,Electrode-Box-A2,3,31,89,-5.2972972972973,28.0613586559532
+Root-beer-2,Electrode-Box-A2,4,60,140,-4.2972972972973,18.4667640613586
+Root-beer-2,Electrode-Box-A2,5,84,147,-3.2972972972973,10.8721694667641
+Vu-vuong,Naive,0,18,61,-8.2972972972973,68.845142439737
+Vu-vuong,Naive,1,35,91,-7.2972972972973,53.2505478451424
+Vu-vuong,Naive,2,61,127,-6.2972972972973,39.6559532505478
+Vu-vuong,Naive,3,34,146,-5.2972972972973,28.0613586559532
+Vu-vuong,Naive,4,61,141,-4.2972972972973,18.4667640613586
+Vu-vuong,Naive,5,37,151,-3.2972972972973,10.8721694667641
+Vu-vuong,Naive,6,49,131,-2.2972972972973,5.27757487216946
+Vu-vuong,Naive,7,65,149,-1.2972972972973,1.68298027757487
+Vu-vuong,Naive,8,67,141,-0.297297297297296,0.0883856829802771
+Vu-vuong,Naive,9,73,140,0.702702702702704,0.493791088385684
+Vu-vuong,Naive,10,72,131,1.7027027027027,2.89919649379109
+Vu-vuong,Naive,11,89,158,2.7027027027027,7.3046018991965
+Vu-vuong,Naive,12,97,154,3.7027027027027,13.7100073046019
+Vu-vuong,Naive,13,93,145,4.7027027027027,22.1154127100073
+Vu-vuong,Naive,14,75,147,5.7027027027027,32.5208181154127
+Vu-vuong,Naive,15,68,142,6.7027027027027,44.9262235208181
+Vu-vuong,Naive,16,96,162,7.7027027027027,59.3316289262235
+Vu-vuong,Naive,17,68,144,8.7027027027027,75.7370343316289
+Vu-vuong,Naive,18,81,134,9.7027027027027,94.1424397370344
+Vu-vuong,Naive,19,66,144,10.7027027027027,114.54784514244
+Vu-vuong,Naive,20,84,127,11.7027027027027,136.953250547845
+Vu-vuong,Naive,21,66,133,12.7027027027027,161.358655953251
diff --git a/analysis/matlab/results/lme_mergeA2_d0_10.txt b/analysis/matlab/results/lme_mergeA2_d0_10.txt
new file mode 100644
index 0000000..209c1ed
--- /dev/null
+++ b/analysis/matlab/results/lme_mergeA2_d0_10.txt
@@ -0,0 +1,70 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeA2_d0_10
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 83 sessions
+day coverage: Box-B2 0..10, Box-A2 0..10
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              83
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    673.84    688.35    -330.92          661.84  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}        15.599       3.7298     4.1822    79    7.4136e-05
+    {'day'        }        8.3258      0.66962     12.434    79    2.7592e-20
+    {'tDCS'       }        4.1737       5.2381    0.79679    79       0.42796
+    {'day:tDCS'   }        1.3833       0.9137      1.514    79       0.13402
+
+
+    Lower       Upper 
+       8.175    23.023
+      6.9929    9.6586
+     -6.2525      14.6
+    -0.43535     3.202
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        13.04       11.2     15.183
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(79)=  1.51  F(1)=   2.292  p=0.134
+days (learning)             t(79)= 12.43  F(1)= 154.593  p=2.759e-20
+tDCS (main, at Day 1)       t(79)=  0.80  F(1)=   0.635  p=0.428
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1340, slope diff=1.38) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=2.8e-20) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.4280) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/lme_mergeA2_d0_13.txt b/analysis/matlab/results/lme_mergeA2_d0_13.txt
new file mode 100644
index 0000000..9eeca59
--- /dev/null
+++ b/analysis/matlab/results/lme_mergeA2_d0_13.txt
@@ -0,0 +1,70 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeA2_d0_13
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 98 sessions
+day coverage: Box-B2 0..13, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              98
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    809.08    824.59    -398.54          797.08  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}        19.458       3.7166    5.2352    94    1.0011e-06
+    {'day'        }        7.1842      0.54693    13.135    94    5.4028e-23
+    {'tDCS'       }         5.841       5.1946    1.1244    94       0.26369
+    {'day:tDCS'   }        1.0024      0.73807    1.3581    94       0.17769
+
+
+    Lower      Upper 
+     12.078    26.837
+     6.0982    8.2701
+     -4.473    16.155
+    -0.4631    2.4678
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        14.123      12.278    16.245
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(94)=  1.36  F(1)=   1.844  p=0.1777
+days (learning)             t(94)= 13.14  F(1)= 172.537  p=5.403e-23
+tDCS (main, at Day 1)       t(94)=  1.12  F(1)=   1.264  p=0.2637
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1777, slope diff=1.00) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=5.4e-23) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.2637) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/lme_mergeA2_full.txt b/analysis/matlab/results/lme_mergeA2_full.txt
new file mode 100644
index 0000000..120a120
--- /dev/null
+++ b/analysis/matlab/results/lme_mergeA2_full.txt
@@ -0,0 +1,73 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeA2_full
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 109 sessions
+day coverage: Box-B2 0..22, Box-A2 0..14
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+** WARNING: the groups' day coverage is UNEQUAL (differ by 8 days). The
+   interaction/slope over this window EXTRAPOLATES the shorter group's line and is
+   CONFOUNDED -- prefer the _d0_14 window (both groups have data throughout). **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             109
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    962.68    978.83    -475.34          950.68  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF     pValue    
+    {'(Intercept)'}         20.701      4.9127     4.2138    105    5.3256e-05
+    {'day'        }         6.8599     0.69872     9.8178    105    1.5726e-16
+    {'tDCS'       }         22.092      6.4372     3.4319    105    0.00085848
+    {'day:tDCS'   }        -2.1793     0.81821    -2.6635    105     0.0089525
+
+
+    Lower      Upper   
+      10.96      30.442
+     5.4745      8.2453
+     9.3279      34.855
+    -3.8016    -0.55692
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1043e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        18.954      16.597    21.644
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(105)= -2.66  F(1)=   7.094  p=0.008953
+days (learning)             t(105)=  9.82  F(1)=  96.388  p=1.573e-16
+tDCS (main, at Day 1)       t(105)=  3.43  F(1)=  11.778  p=0.0008585
+
+INTERPRETATION
+  - days x tDCS interaction: SIGNIFICANT (p=0.0090, slope diff=-2.18) -> the tDCS (Box-B2) group improves SLOWER -- groups CONVERGE (Box-B2 is ahead early, the gap narrows).
+  - days (learning): SIGNIFICANT (p=1.6e-16) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): SIGNIFICANT (p=0.0009) -> the groups already DIFFER on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/lme_mergeB2_d0_10.txt b/analysis/matlab/results/lme_mergeB2_d0_10.txt
new file mode 100644
index 0000000..b7971b8
--- /dev/null
+++ b/analysis/matlab/results/lme_mergeB2_d0_10.txt
@@ -0,0 +1,70 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_d0_10
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 83 sessions
+day coverage: Box-B2 0..10, Box-A2 0..10
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              83
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    669.06    683.57    -328.53          657.06  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue   
+    {'(Intercept)'}         17.318      5.1725      3.348    79    0.0012488
+    {'day'        }         7.9975     0.75795     10.551    79    9.547e-17
+    {'tDCS'       }        0.42782      6.5088    0.06573    79      0.94776
+    {'day:tDCS'   }         1.7407     0.91342     1.9057    79     0.060324
+
+
+    Lower       Upper 
+      7.0221    27.613
+      6.4888    9.5061
+     -12.528    13.383
+    -0.07739    3.5588
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7075  
+
+
+    Lower     Upper 
+    2.7829    11.705
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        11.955      10.184    14.034
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(79)=  1.91  F(1)=   3.632  p=0.06032
+days (learning)             t(79)= 10.55  F(1)= 111.334  p=9.547e-17
+tDCS (main, at Day 1)       t(79)=  0.07  F(1)=   0.004  p=0.9478
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.0603, slope diff=1.74) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=9.5e-17) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.9478) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/lme_mergeB2_d0_13.txt b/analysis/matlab/results/lme_mergeB2_d0_13.txt
new file mode 100644
index 0000000..29be387
--- /dev/null
+++ b/analysis/matlab/results/lme_mergeB2_d0_13.txt
@@ -0,0 +1,70 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_d0_13
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 98 sessions
+day coverage: Box-B2 0..13, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              98
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    803.97    819.48    -395.99          791.97  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}        21.528       5.1581     4.1736    94    6.6907e-05
+    {'day'        }        6.6603      0.67208       9.91    94     2.855e-16
+    {'tDCS'       }        1.8428       6.4496    0.28572    94       0.77572
+    {'day:tDCS'   }        1.5375      0.78742     1.9525    94      0.053848
+
+
+    Lower        Upper 
+       11.286    31.769
+       5.3259    7.9948
+      -10.963    14.649
+    -0.025966    3.1009
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.4251  
+
+
+    Lower     Upper
+    2.6045    11.3 
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        13.148      11.362    15.215
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(94)=  1.95  F(1)=   3.812  p=0.05385
+days (learning)             t(94)=  9.91  F(1)=  98.208  p=2.855e-16
+tDCS (main, at Day 1)       t(94)=  0.29  F(1)=   0.082  p=0.7757
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.0538, slope diff=1.54) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=2.9e-16) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.7757) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/lme_mergeB2_full.txt b/analysis/matlab/results/lme_mergeB2_full.txt
new file mode 100644
index 0000000..be7ca25
--- /dev/null
+++ b/analysis/matlab/results/lme_mergeB2_full.txt
@@ -0,0 +1,73 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_mergeB2_full
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 8 subjects, 109 sessions
+day coverage: Box-B2 0..22, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+** WARNING: the groups' day coverage is UNEQUAL (differ by 9 days). The
+   interaction/slope over this window EXTRAPOLATES the shorter group's line and is
+   CONFOUNDED -- prefer the _d0_13 window (both groups have data throughout). **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             109
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    967.24    983.39    -477.62          955.24  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF     pValue    
+    {'(Intercept)'}           21.7     5.9981     3.6178    105    0.00045861
+    {'day'        }         6.4863     0.9412     6.8916    105    4.2336e-10
+    {'tDCS'       }         17.493     7.1288     2.4538    105      0.015779
+    {'day:tDCS'   }        -1.4651      1.025    -1.4294    105       0.15586
+
+
+    Lower      Upper  
+     9.8069     33.593
+     4.6201     8.3526
+     3.3578     31.628
+    -3.4974    0.56726
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1487e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        19.354      16.948    22.101
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(105)= -1.43  F(1)=   2.043  p=0.1559
+days (learning)             t(105)=  6.89  F(1)=  47.494  p=4.234e-10
+tDCS (main, at Day 1)       t(105)=  2.45  F(1)=   6.021  p=0.01578
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1559, slope diff=-1.47) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=4.2e-10) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): SIGNIFICANT (p=0.0158) -> the groups already DIFFER on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/lme_unmerged_d0_10.txt b/analysis/matlab/results/lme_unmerged_d0_10.txt
new file mode 100644
index 0000000..87e83fc
--- /dev/null
+++ b/analysis/matlab/results/lme_unmerged_d0_10.txt
@@ -0,0 +1,70 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_d0_10
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 6 subjects, 61 sessions
+day coverage: Box-B2 0..10, Box-A2 0..10
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              61
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    499.59    512.25    -243.79          487.59  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}        17.372       5.1906     3.3469    57     0.0014518
+    {'day'        }        7.9666      0.79053     10.077    57    2.8317e-14
+    {'tDCS'       }        4.9763       7.2862    0.68298    57       0.49739
+    {'day:tDCS'   }        1.5577       1.0483     1.4858    57       0.14283
+
+
+    Lower      Upper 
+     6.9783    27.766
+     6.3836    9.5496
+     -9.614    19.567
+    -0.5416    3.6569
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.3536  
+
+
+    Lower     Upper 
+    2.1542    13.305
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        12.508      10.37    15.086
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(57)=  1.49  F(1)=   2.208  p=0.1428
+days (learning)             t(57)= 10.08  F(1)= 101.556  p=2.832e-14
+tDCS (main, at Day 1)       t(57)=  0.68  F(1)=   0.466  p=0.4974
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1428, slope diff=1.56) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=2.8e-14) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.4974) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/lme_unmerged_d0_13.txt b/analysis/matlab/results/lme_unmerged_d0_13.txt
new file mode 100644
index 0000000..324970d
--- /dev/null
+++ b/analysis/matlab/results/lme_unmerged_d0_13.txt
@@ -0,0 +1,70 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_d0_13
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 6 subjects, 70 sessions
+day coverage: Box-B2 0..13, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              70
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    586.72    600.22    -287.36          574.72  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF    pValue    
+    {'(Intercept)'}          21.7       4.5484     4.7709    66    1.0537e-05
+    {'day'        }        6.4863      0.71372     9.0881    66    3.0346e-13
+    {'tDCS'       }        6.0226       6.3135    0.95392    66        0.3436
+    {'day:tDCS'   }        1.5467      0.93755     1.6497    66       0.10376
+
+
+    Lower       Upper 
+      12.619    30.781
+      5.0614    7.9113
+     -6.5827    18.628
+    -0.32523    3.4185
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0       
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper
+    {'Res Std'}        14.676      12.436    17.32
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(66)=  1.65  F(1)=   2.721  p=0.1038
+days (learning)             t(66)=  9.09  F(1)=  82.593  p=3.035e-13
+tDCS (main, at Day 1)       t(66)=  0.95  F(1)=   0.910  p=0.3436
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.1038, slope diff=1.55) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=3e-13) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.3436) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/lme_unmerged_full.txt b/analysis/matlab/results/lme_unmerged_full.txt
new file mode 100644
index 0000000..a6f2630
--- /dev/null
+++ b/analysis/matlab/results/lme_unmerged_full.txt
@@ -0,0 +1,70 @@
+==============================================================================
+LINEAR MIXED MODEL (days x tDCS) -- scenario: lme_unmerged_full
+==============================================================================
+model: success ~ day * tDCS + (1|subject)   [tDCS: Electrode-Box-B2 = 1 vs Electrode-Box-A2 = 0]
+N = 6 subjects, 71 sessions
+day coverage: Box-B2 0..14, Box-A2 0..13
+(day is raw and 0-indexed: our day 0 = the paper's "Day 1", so the tDCS
+ main effect below is the group difference on Day 1 -- comparable to the paper.)
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: success ~ day*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              71
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + day*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    596.33    609.9    -292.16          584.33  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}          21.53      5.5382    3.8874    67    0.00023506
+    {'day'        }         6.6587     0.72367    9.2014    67    1.6706e-13
+    {'tDCS'       }         8.0204      7.6977    1.0419    67        0.3012
+    {'day:tDCS'   }        0.93747      0.9187    1.0204    67        0.3112
+
+
+    Lower       Upper 
+      10.475    32.584
+      5.2143    8.1032
+     -7.3444    23.385
+    -0.89627    2.7712
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7931  
+
+
+    Lower     Upper 
+    2.4723    13.574
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        14.163      11.93    16.813
+
+
+effect                      t (df)         F (df1)       p
+----------------------------------------------------------------------
+days x tDCS (interaction)   t(67)=  1.02  F(1)=   1.041  p=0.3112
+days (learning)             t(67)=  9.20  F(1)=  84.665  p=1.671e-13
+tDCS (main, at Day 1)       t(67)=  1.04  F(1)=   1.086  p=0.3012
+
+INTERPRETATION
+  - days x tDCS interaction: n.s. (p=0.3112, slope diff=0.94) -> slopes are parallel (no differential change over training).
+  - days (learning): SIGNIFICANT (p=1.7e-13) -> performance improves with training.
+  - tDCS main effect on Day 1 (our day 0): n.s. (p=0.3012) -> the groups are comparable (as in the paper) on Day 1.
+
+Paper reference (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008;
+  days t(227)=9.64, F(1)=267.64, p=1.2e-18; tDCS t(227)=0.23, F(1)=0.053, p=0.81.
+  (Our N and exact statistics differ; this replicates the MODEL FORM on our data.)
diff --git a/analysis/matlab/results/mergeA2_d0_10.txt b/analysis/matlab/results/mergeA2_d0_10.txt
new file mode 100644
index 0000000..9a76f7e
--- /dev/null
+++ b/analysis/matlab/results/mergeA2_d0_10.txt
@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeA2_d0_10
+==============================================================================
+merge key: mergeA2   day window: 0..10   observations: 126
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            4         44          68.3      0.541       10
+Electrode-Box-A2            4         39          54.0      0.452       10
+Naive                       4         43          46.8      0.440       10
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    297.29    314.31    -142.65          285.29  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat       DF 
+    {'(Intercept)'           }           4.2656     0.092258      46.236    121
+    {'group_Electrode-Box-A2'}         -0.12664      0.12996    -0.97447    121
+    {'group_Naive'           }         -0.41983      0.12988     -3.2324    121
+    {'day_c'                 }          0.20187    0.0052718      38.293    121
+    {'day_c2'                }        -0.024068    0.0015714     -15.317    121
+
+
+    pValue        Lower        Upper    
+    9.3543e-79        4.083       4.4483
+       0.33177     -0.38392      0.13065
+     0.0015818     -0.67696      -0.1627
+    1.7547e-69      0.19144      0.21231
+    4.2028e-30    -0.027179    -0.020957
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17893 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             124
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    363.74    380.67    -175.87          351.74  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat      DF 
+    {'(Intercept)'           }          0.15282       0.1244     1.2285    119
+    {'group_Electrode-Box-A2'}         -0.28467      0.17474    -1.6291    119
+    {'group_Naive'           }         -0.57945      0.17473    -3.3162    119
+    {'day_c'                 }          0.21677    0.0069268     31.294    119
+    {'day_c2'                }        -0.013732    0.0021989    -6.2451    119
+
+
+    pValue        Lower        Upper     
+       0.22169      -0.0935       0.39915
+       0.10595     -0.63068      0.061343
+     0.0012101     -0.92543      -0.23346
+    2.8838e-59      0.20305       0.23048
+    6.7844e-09    -0.018086    -0.0093781
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.23963 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    304.66    327.35    -144.33          288.66  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate     SE           tStat  
+    {'(Intercept)'                 }           4.2968     0.091824     46.794
+    {'group_Electrode-Box-A2'      }         -0.15174      0.12947     -1.172
+    {'group_Naive'                 }         -0.50686      0.13029    -3.8902
+    {'day_c'                       }          0.18436    0.0072535     25.417
+    {'day_c2'                      }        -0.024459     0.001577    -15.509
+    {'group_Electrode-Box-A2:day_c'}         0.014228     0.010876     1.3082
+    {'group_Naive:day_c'           }         0.051203     0.011091     4.6167
+
+
+    DF     pValue        Lower         Upper    
+    119    1.7772e-78         4.115       4.4786
+    119       0.24354       -0.4081      0.10462
+    119    0.00016554      -0.76486     -0.24887
+    119    6.5517e-50          0.17      0.19872
+    119    2.3835e-30     -0.027581    -0.021336
+    119       0.19332    -0.0073071     0.035763
+    119    9.9271e-06      0.029242     0.073164
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17742 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=9.71, Box-A2 mean=9.18
+  Welch two-sided p=0.556, Mann-Whitney p=0.686 (nB=4, nA=4)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0001, but with just
+ observation-level DF (df2=119) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.14x Box-A2  (one-sided p=0.1649)  -> not supported
+  [rate/level ] Box-B2 = 1.33x Box-A2  (one-sided p=0.0517)  -> not supported
+
+Per-animal (Box-B2 n=4 vs Box-A2 n=4), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0230, Mann-Whitney one-sided (B2>A2) p=0.0143
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0689, Mann-Whitney one-sided (B2>A2) p=0.0571
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.
diff --git a/analysis/matlab/results/mergeA2_full.txt b/analysis/matlab/results/mergeA2_full.txt
new file mode 100644
index 0000000..5eafbc4
--- /dev/null
+++ b/analysis/matlab/results/mergeA2_full.txt
@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeA2_full
+==============================================================================
+merge key: mergeA2   day window: 0..26   observations: 185
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            4         63          80.2      0.605       22
+Electrode-Box-A2            4         46          60.4      0.484       14
+Naive                       4         76          59.6      0.492       26
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    564.95    584.27    -276.47          552.95  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat      DF 
+    {'(Intercept)'           }            4.5232      0.058089     77.867    180
+    {'group_Electrode-Box-A2'}           -0.1219      0.083374    -1.4621    180
+    {'group_Naive'           }          -0.36897      0.082023    -4.4984    180
+    {'day_c'                 }           0.10078      0.002307     43.684    180
+    {'day_c2'                }        -0.0064744    0.00024857    -26.047    180
+
+
+    pValue         Lower         Upper     
+    1.4699e-140        4.4086        4.6379
+        0.14547      -0.28641      0.042619
+     1.2247e-05      -0.53082      -0.20712
+     9.6816e-98      0.096227       0.10533
+     5.8173e-63    -0.0069649    -0.0059839
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.11179 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             183
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    661.66    680.91    -324.83          649.66  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat      DF 
+    {'(Intercept)'           }           0.62325       0.10794     5.7739    178
+    {'group_Electrode-Box-A2'}          -0.33496       0.15306    -2.1884    178
+    {'group_Naive'           }          -0.64512       0.15201    -4.2441    178
+    {'day_c'                 }           0.12541     0.0034219     36.649    178
+    {'day_c2'                }        -0.0069611    0.00035344    -19.695    178
+
+
+    pValue        Lower         Upper     
+    3.3843e-08       0.41024       0.83626
+      0.029941      -0.63701     -0.032914
+    3.5275e-05      -0.94509      -0.34516
+    7.5329e-85       0.11866       0.13216
+    1.4145e-46    -0.0076585    -0.0062636
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.20951 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    557.88    583.64    -270.94          541.88  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate      SE           tStat  
+    {'(Intercept)'                 }            4.5419     0.060057     75.626
+    {'group_Electrode-Box-A2'      }          -0.13686     0.086298    -1.5859
+    {'group_Naive'                 }          -0.41002     0.085075    -4.8196
+    {'day_c'                       }          0.090967    0.0032463     28.021
+    {'day_c2'                      }        -0.0068957    0.0002769    -24.904
+    {'group_Electrode-Box-A2:day_c'}         0.0071293    0.0064228       1.11
+    {'group_Naive:day_c'           }          0.023329    0.0043517     5.3609
+
+
+    DF     pValue         Lower         Upper     
+    178    3.0366e-137        4.4233        4.6604
+    178        0.11453      -0.30716      0.033436
+    178     3.0699e-06      -0.57791      -0.24214
+    178     3.6033e-67      0.084561      0.097373
+    178     6.7558e-60    -0.0074422    -0.0063493
+    178         0.2685    -0.0055454      0.019804
+    178     2.5437e-07      0.014742      0.031917
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.11568 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=6.92, Box-A2 mean=7.95
+  Welch two-sided p=0.531, Mann-Whitney p=0.886 (nB=4, nA=4)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=178) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.13x Box-A2  (one-sided p=0.0719)  -> not supported
+  [rate/level ] Box-B2 = 1.40x Box-A2  (one-sided p=0.0143)  -> SUPPORTED
+
+Per-animal (Box-B2 n=4 vs Box-A2 n=4), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0348, Mann-Whitney one-sided (B2>A2) p=0.0286
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0555, Mann-Whitney one-sided (B2>A2) p=0.0286
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.
diff --git a/analysis/matlab/results/mergeB2_d0_10.txt b/analysis/matlab/results/mergeB2_d0_10.txt
new file mode 100644
index 0000000..872247e
--- /dev/null
+++ b/analysis/matlab/results/mergeB2_d0_10.txt
@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeB2_d0_10
+==============================================================================
+merge key: mergeB2   day window: 0..10   observations: 126
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            5         55          66.4      0.533       10
+Electrode-Box-A2            3         28          52.1      0.433       10
+Naive                       4         43          46.8      0.440       10
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    297.84    314.85    -142.92          285.84  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat       DF 
+    {'(Intercept)'           }           4.2369      0.08452      50.129    121
+    {'group_Electrode-Box-A2'}        -0.091837      0.13759    -0.66748    121
+    {'group_Naive'           }         -0.39133      0.12596     -3.1068    121
+    {'day_c'                 }           0.2019    0.0052735      38.287    121
+    {'day_c2'                }        -0.024069    0.0015714     -15.317    121
+
+
+    pValue        Lower       Upper    
+     8.553e-83      4.0696       4.4042
+       0.50574    -0.36423      0.18055
+     0.0023571    -0.64071     -0.14196
+     1.787e-69     0.19146      0.21235
+    4.1977e-30    -0.02718    -0.020958
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.18296 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             124
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    364.15    381.07    -176.07          352.15  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat      DF 
+    {'(Intercept)'           }          0.11026      0.11374    0.96938    119
+    {'group_Electrode-Box-A2'}         -0.26616      0.18448    -1.4427    119
+    {'group_Naive'           }         -0.53698      0.16912    -3.1751    119
+    {'day_c'                 }          0.21673    0.0069278     31.283    119
+    {'day_c2'                }        -0.013726    0.0021988    -6.2424    119
+
+
+    pValue        Lower       Upper    
+       0.33432    -0.11496      0.33548
+       0.15172    -0.63144      0.09913
+     0.0019073    -0.87186      -0.2021
+    2.9856e-59     0.20301      0.23044
+    6.8729e-09    -0.01808    -0.009372
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.24468 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    304.98    327.67    -144.49          288.98  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate      SE           tStat   
+    {'(Intercept)'                 }            4.2592     0.082682      51.513
+    {'group_Electrode-Box-A2'      }          -0.10296      0.13455    -0.76522
+    {'group_Naive'                 }          -0.46886       0.1243     -3.7722
+    {'day_c'                       }            0.1907    0.0067258      28.354
+    {'day_c2'                      }          -0.02454    0.0015791     -15.541
+    {'group_Electrode-Box-A2:day_c'}        -0.0023862     0.012062    -0.19783
+    {'group_Naive:day_c'           }          0.045036     0.010729      4.1975
+
+
+    DF     pValue        Lower        Upper    
+    119    3.2785e-83       4.0955        4.423
+    119       0.44566     -0.36937      0.16346
+    119    0.00025365     -0.71498     -0.22275
+    119     9.114e-55      0.17738      0.20402
+    119    2.0273e-30    -0.027667    -0.021413
+    119       0.84352     -0.02627     0.021498
+    119    5.2317e-05     0.023791     0.066281
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17816 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=9.74, Box-A2 mean=8.96
+  Welch two-sided p=0.517, Mann-Whitney p=0.571 (nB=5, nA=3)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0001, but with just
+ observation-level DF (df2=119) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.10x Box-A2  (one-sided p=0.2522)  -> not supported
+  [rate/level ] Box-B2 = 1.30x Box-A2  (one-sided p=0.0745)  -> not supported
+
+Per-animal (Box-B2 n=5 vs Box-A2 n=3), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0228, Mann-Whitney one-sided (B2>A2) p=0.0179
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0943, Mann-Whitney one-sided (B2>A2) p=0.0714
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.
diff --git a/analysis/matlab/results/mergeB2_full.txt b/analysis/matlab/results/mergeB2_full.txt
new file mode 100644
index 0000000..753a25c
--- /dev/null
+++ b/analysis/matlab/results/mergeB2_full.txt
@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeB2_full
+==============================================================================
+merge key: mergeB2   day window: 0..26   observations: 185
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            5         78          78.4      0.596       22
+Electrode-Box-A2            3         31          55.4      0.448       13
+Naive                       4         76          59.6      0.492       26
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    565.84    585.16    -276.92          553.84  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE            tStat      DF 
+    {'(Intercept)'           }           4.4986      0.054066     83.206    180
+    {'group_Electrode-Box-A2'}        -0.096187       0.09052    -1.0626    180
+    {'group_Naive'           }         -0.34448      0.080981    -4.2538    180
+    {'day_c'                 }          0.10082     0.0023113     43.621    180
+    {'day_c2'                }         -0.00647    0.00024877    -26.008    180
+
+
+    pValue         Lower         Upper     
+    1.3263e-145        4.3919        4.6053
+        0.28939       -0.2748       0.08243
+     3.3739e-05      -0.50427      -0.18468
+     1.2306e-97      0.096261       0.10538
+     7.2052e-63    -0.0069609    -0.0059791
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.1166  
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             183
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    661.88    681.14    -324.94          649.88  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat      DF 
+    {'(Intercept)'           }           0.57975      0.098046      5.913    178
+    {'group_Electrode-Box-A2'}          -0.33262       0.16122    -2.0631    178
+    {'group_Naive'           }          -0.60174       0.14641    -4.1098    178
+    {'day_c'                 }           0.12534     0.0034238     36.607    178
+    {'day_c2'                }        -0.0069519    0.00035349    -19.666    178
+
+
+    pValue        Lower         Upper     
+    1.6792e-08       0.38627       0.77323
+      0.040554      -0.65077     -0.014466
+    6.0348e-05      -0.89067      -0.31281
+    9.0274e-85       0.11858       0.13209
+    1.6927e-46    -0.0076494    -0.0062543
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.21289 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    560.22    585.99    -272.11          544.22  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate     SE            tStat  
+    {'(Intercept)'                 }            4.516      0.055699     81.079
+    {'group_Electrode-Box-A2'      }         -0.11558      0.094376    -1.2247
+    {'group_Naive'                 }         -0.38342      0.083677    -4.5821
+    {'day_c'                       }         0.092386     0.0030001     30.794
+    {'day_c2'                      }        -0.006943    0.00027446    -25.297
+    {'group_Electrode-Box-A2:day_c'}        0.0036285     0.0081693    0.44417
+    {'group_Naive:day_c'           }         0.022361     0.0042611     5.2476
+
+
+    DF     pValue        Lower         Upper     
+    178    1.781e-142        4.4061        4.6259
+    178       0.22231      -0.30182      0.070658
+    178    8.6367e-06      -0.54854      -0.21829
+    178    3.1867e-73      0.086466      0.098307
+    178    7.6668e-61    -0.0074846    -0.0064014
+    178       0.65746     -0.012493       0.01975
+    178    4.3478e-07      0.013952       0.03077
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.12017 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=6.98, Box-A2 mean=8.19
+  Welch two-sided p=0.508, Mann-Whitney p=0.786 (nB=5, nA=3)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=178) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.10x Box-A2  (one-sided p=0.1440)  -> not supported
+  [rate/level ] Box-B2 = 1.39x Box-A2  (one-sided p=0.0196)  -> SUPPORTED
+
+Per-animal (Box-B2 n=5 vs Box-A2 n=3), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0204, Mann-Whitney one-sided (B2>A2) p=0.0179
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0440, Mann-Whitney one-sided (B2>A2) p=0.0179
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.
diff --git a/analysis/matlab/results/mergeNaive_full.txt b/analysis/matlab/results/mergeNaive_full.txt
new file mode 100644
index 0000000..bd7ad09
--- /dev/null
+++ b/analysis/matlab/results/mergeNaive_full.txt
@@ -0,0 +1,199 @@
+==============================================================================
+tDCS GLM report -- scenario: mergeNaive_full
+==============================================================================
+merge key: mergeNaive   day window: 0..26   observations: 185
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            4         63          80.2      0.605       22
+Electrode-Box-A             1         15          70.7      0.557       14
+Electrode-Box-A2            7        107          58.4      0.479       26
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    569.28    588.6    -278.64          557.28  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat   
+    {'(Intercept)'           }            4.5235      0.074032      61.102
+    {'group_Electrode-Box-A' }          -0.12346       0.16529    -0.74694
+    {'group_Electrode-Box-A2'}          -0.26481      0.093039     -2.8462
+    {'day_c'                 }           0.10065     0.0023081      43.608
+    {'day_c2'                }        -0.0064638    0.00024915     -25.944
+
+
+    DF     pValue         Lower         Upper     
+    180    2.6809e-122        4.3774        4.6696
+    180        0.45607      -0.44962        0.2027
+    180      0.0049387      -0.44839     -0.081217
+    180     1.2924e-97      0.096096       0.10521
+    180     1.0244e-62    -0.0069555    -0.0059722
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.14464 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             183
+    Fixed effects coefficients           5
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    663.16    682.41    -325.58          651.16  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat   
+    {'(Intercept)'           }           0.62291       0.11692      5.3275
+    {'group_Electrode-Box-A' }          -0.21631       0.26048    -0.83042
+    {'group_Electrode-Box-A2'}           -0.5314       0.14632     -3.6318
+    {'day_c'                 }           0.12505      0.003415      36.618
+    {'day_c2'                }        -0.0069489    0.00035361     -19.651
+
+
+    DF     pValue        Lower         Upper     
+    178     2.981e-07       0.39218       0.85365
+    178       0.40742      -0.73034       0.29772
+    178    0.00036793      -0.82014      -0.24265
+    178    8.6013e-85       0.11831       0.13179
+    178    1.8602e-46    -0.0076467    -0.0062511
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.22797 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    561.97    587.73    -272.98          545.97  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate      SE            tStat   
+    {'(Intercept)'                 }            4.5372      0.083001      54.664
+    {'group_Electrode-Box-A'       }          -0.13394       0.18535    -0.72264
+    {'group_Electrode-Box-A2'      }          -0.27771       0.10424     -2.6641
+    {'day_c'                       }          0.090249     0.0032246      27.988
+    {'day_c2'                      }        -0.0066704    0.00025769     -25.885
+    {'group_Electrode-Box-A:day_c' }          0.010369     0.0086089      1.2044
+    {'group_Electrode-Box-A2:day_c'}          0.020125     0.0041158      4.8897
+
+
+    DF     pValue         Lower         Upper     
+    178    3.3741e-113        4.3734         4.701
+    178        0.47085       -0.4997       0.23182
+    178      0.0084289      -0.48341     -0.071999
+    178     4.2889e-67      0.083886      0.096613
+    178     3.0498e-62    -0.0071789    -0.0061619
+    178        0.23002    -0.0066197      0.027358
+    178     2.2462e-06      0.012003      0.028247
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.16287 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=6.92, Box-A2 mean=6.25
+  Welch two-sided p=0.689, Mann-Whitney p=0.527 (nB=4, nA=7)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=178) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.30x Box-A2  (one-sided p=0.0022)  -> SUPPORTED
+  [rate/level ] Box-B2 = 1.70x Box-A2  (one-sided p=0.0001)  -> SUPPORTED
+
+Per-animal (Box-B2 n=4 vs Box-A2 n=7), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0158, Mann-Whitney one-sided (B2>A2) p=0.0030
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0207, Mann-Whitney one-sided (B2>A2) p=0.0030
+
+(No unknown groups -- they were merged into the anchors; only the H2 anchor
+contrast applies.)
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.
diff --git a/analysis/matlab/results/paper_mergeA2.txt b/analysis/matlab/results/paper_mergeA2.txt
new file mode 100644
index 0000000..3ab9756
--- /dev/null
+++ b/analysis/matlab/results/paper_mergeA2.txt
@@ -0,0 +1,67 @@
+==============================================================================
+PAPER LME REPLICATION -- mergeA2
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+N = 8 rats, 109 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..22, control(A2) 0..14
+** WARNING: unequal day coverage -- the full-range stim:day interaction
+   extrapolates the control group's line and is CONFOUNDED here (the paper's
+   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             109
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    962.68    978.83    -475.34          950.68  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat      DF     pValue    
+    {'(Intercept)'}         20.701      4.9127     4.2138    105    5.3256e-05
+    {'day'        }         6.8599     0.69872     9.8178    105    1.5726e-16
+    {'stim'       }         22.092      6.4372     3.4319    105    0.00085848
+    {'day:stim'   }        -2.1793     0.81821    -2.6635    105     0.0089525
+
+
+    Lower      Upper   
+      10.96      30.442
+     5.4745      8.2453
+     9.3279      34.855
+    -3.8016    -0.55692
+
+Random effects covariance parameters (95% CIs):
+Group: rat (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1043e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        18.954      16.597    21.644
+
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(105)= -2.66  F(1)=   7.094  p=0.008953
+day (learning)             t(105)=  9.82  F(1)=  96.388  p=1.573e-16
+stim (main, Day 1)         t(105)=  3.43  F(1)=  11.778  p=0.0008585
+interaction 95% CI: [-3.80, -0.56]
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.
diff --git a/analysis/matlab/results/paper_mergeB2.txt b/analysis/matlab/results/paper_mergeB2.txt
new file mode 100644
index 0000000..b72684c
--- /dev/null
+++ b/analysis/matlab/results/paper_mergeB2.txt
@@ -0,0 +1,67 @@
+==============================================================================
+PAPER LME REPLICATION -- mergeB2
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+N = 8 rats, 109 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..22, control(A2) 0..13
+** WARNING: unequal day coverage -- the full-range stim:day interaction
+   extrapolates the control group's line and is CONFOUNDED here (the paper's
+   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             109
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    967.24    983.39    -477.62          955.24  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF     pValue    
+    {'(Intercept)'}           21.7     5.9981     3.6178    105    0.00045861
+    {'day'        }         6.4863     0.9412     6.8916    105    4.2336e-10
+    {'stim'       }         17.493     7.1288     2.4538    105      0.015779
+    {'day:stim'   }        -1.4651      1.025    -1.4294    105       0.15586
+
+
+    Lower      Upper  
+     9.8069     33.593
+     4.6201     8.3526
+     3.3578     31.628
+    -3.4974    0.56726
+
+Random effects covariance parameters (95% CIs):
+Group: rat (8 Levels)
+    Name1                  Name2                  Type           Estimate  
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        2.1487e-15
+
+
+    Lower    Upper
+    NaN      NaN  
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        19.354      16.948    22.101
+
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(105)= -1.43  F(1)=   2.043  p=0.1559
+day (learning)             t(105)=  6.89  F(1)=  47.494  p=4.234e-10
+stim (main, Day 1)         t(105)=  2.45  F(1)=   6.021  p=0.01578
+interaction 95% CI: [-3.50, +0.57]
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.
diff --git a/analysis/matlab/results/paper_mergeNaive.txt b/analysis/matlab/results/paper_mergeNaive.txt
new file mode 100644
index 0000000..965a056
--- /dev/null
+++ b/analysis/matlab/results/paper_mergeNaive.txt
@@ -0,0 +1,67 @@
+==============================================================================
+PAPER LME REPLICATION -- mergeNaive
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+N = 11 rats, 170 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..22, control(A2) 0..26
+** WARNING: unequal day coverage -- the full-range stim:day interaction
+   extrapolates the control group's line and is CONFOUNDED here (the paper's
+   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations             170
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    1526.4    1545.2    -757.2           1514.4  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF     pValue    
+    {'(Intercept)'}        30.553       4.7932    6.3742    166    1.7469e-09
+    {'day'        }        3.5345      0.33399    10.583    166    2.5084e-20
+    {'stim'       }        10.155       8.0021    1.2691    166        0.2062
+    {'day:stim'   }        1.5835      0.58611    2.7018    166     0.0076138
+
+
+    Lower      Upper 
+     21.089    40.016
+     2.8751    4.1939
+    -5.6439    25.954
+    0.42633    2.7407
+
+Random effects covariance parameters (95% CIs):
+Group: rat (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.2449  
+
+
+    Lower    Upper 
+    5.388    15.863
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        19.866      17.802    22.168
+
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(166)=  2.70  F(1)=   7.299  p=0.007614
+day (learning)             t(166)= 10.58  F(1)= 111.990  p=2.508e-20
+stim (main, Day 1)         t(166)=  1.27  F(1)=   1.611  p=0.2062
+interaction 95% CI: [+0.43, +2.74]
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.
diff --git a/analysis/matlab/results/paper_unmerged.txt b/analysis/matlab/results/paper_unmerged.txt
new file mode 100644
index 0000000..4c25bb6
--- /dev/null
+++ b/analysis/matlab/results/paper_unmerged.txt
@@ -0,0 +1,64 @@
+==============================================================================
+PAPER LME REPLICATION -- unmerged
+==============================================================================
+model: behavior ~ stim + day + stim:day + (1|rat)   [stim: Electrode-Box-B2=1 vs Electrode-Box-A2=0]
+N = 6 rats, 71 sessions   (day raw; day 0 = paper "Day 1")
+day coverage: stim(B2) 0..14, control(A2) 0..13
+
+==============================================================================
+FULL MODEL SUMMARY -- fitlme: behavior ~ stim + day + stim:day + (1|rat)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              71
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    behavior ~ 1 + day*stim + (1 | rat)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    596.33    609.9    -292.16          584.33  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}          21.53      5.5382    3.8874    67    0.00023506
+    {'day'        }         6.6587     0.72367    9.2014    67    1.6706e-13
+    {'stim'       }         8.0204      7.6977    1.0419    67        0.3012
+    {'day:stim'   }        0.93747      0.9187    1.0204    67        0.3112
+
+
+    Lower       Upper 
+      10.475    32.584
+      5.2143    8.1032
+     -7.3444    23.385
+    -0.89627    2.7712
+
+Random effects covariance parameters (95% CIs):
+Group: rat (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7931  
+
+
+    Lower     Upper 
+    2.4723    13.574
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        14.163      11.93    16.813
+
+
+
+effect                     t (df)        F (df1)       p
+------------------------------------------------------------------
+stim x day (interaction)   t(67)=  1.02  F(1)=   1.041  p=0.3112
+day (learning)             t(67)=  9.20  F(1)=  84.665  p=1.671e-13
+stim (main, Day 1)         t(67)=  1.04  F(1)=   1.086  p=0.3012
+interaction 95% CI: [-0.90, +2.77]
+
+Paper (N=24): interaction t(227)=2.68, F(1)=7.12, p=0.008; day t(227)=9.64,
+  F(1)=267.64, p=1.2e-18; stim t(227)=0.23, F(1)=0.053, p=0.81.
diff --git a/analysis/matlab/results/phase_mergeA2.txt b/analysis/matlab/results/phase_mergeA2.txt
new file mode 100644
index 0000000..cb5d9f4
--- /dev/null
+++ b/analysis/matlab/results/phase_mergeA2.txt
@@ -0,0 +1,155 @@
+==============================================================================
+PHASED days x tDCS LME -- mergeA2 (Box-B2 vs Box-A2)
+==============================================================================
+model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
+
+phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
+--------------------------------------------------------------------------------------------
+0-5      8   10.54   15.16  1.5e-10   0.693        0.014         +4.62 [+1.00, +8.24]
+6-10     7    3.17    4.83  0.035     0.127        0.390         +1.66 [-2.22, +5.53]
+6-13     7    2.92    4.47  0.00032   0.125        0.118         +1.55 [-0.41, +3.52]
+
+Note: the interaction p (and CI) use fitlme observation-level DF and are
+ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
+phase carries the Box-B2 faster-acquisition signal; late phases converge.
+==============================================================================
+FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              48
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    381.01    392.23    -184.5           369.01  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF    pValue    
+    {'(Intercept)'}          10.06      4.465      2.253    44      0.029296
+    {'dayp'       }         10.543       1.27     8.3016    44    1.4943e-10
+    {'tDCS'       }        -2.5119     6.3144    -0.3978    44        0.6927
+    {'dayp:tDCS'  }         4.6214      1.796     2.5731    44      0.013526
+
+
+    Lower      Upper 
+      1.061    19.058
+     7.9834    13.102
+    -15.238    10.214
+     1.0018    8.2411
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        4.5394  
+
+
+    Lower     Upper 
+    1.7406    11.838
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.625      8.5345    13.229
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              35
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    265.24    274.57    -126.62          253.24  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF    pValue    
+    {'(Intercept)'}        75.867      4.9313     15.385    31    4.6291e-16
+    {'dayp'       }        3.1667      1.4366     2.2043    31      0.035054
+    {'tDCS'       }        10.233      6.5234     1.5687    31       0.12687
+    {'dayp:tDCS'  }        1.6583      1.9004    0.87262    31       0.38958
+
+
+    Lower      Upper 
+     65.809    85.924
+    0.23676    6.0966
+    -3.0713    23.538
+    -2.2176    5.5342
+
+Random effects covariance parameters (95% CIs):
+Group: subject (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.9837  
+
+
+    Lower     Upper
+    2.9396    12.18
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.8684      6.0554    10.224
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              50
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    367.22    378.69    -177.61          355.22  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}          76.4       4.9765    15.352    46    1.0096e-19
+    {'dayp'       }        2.9189      0.74973    3.8932    46    0.00031772
+    {'tDCS'       }        10.289       6.5773    1.5643    46       0.12459
+    {'dayp:tDCS'  }         1.554      0.97562    1.5928    46       0.11805
+
+
+    Lower       Upper 
+      66.382    86.417
+      1.4097     4.428
+     -2.9502    23.528
+    -0.40985    3.5178
+
+Random effects covariance parameters (95% CIs):
+Group: subject (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        7.1357  
+
+
+    Lower     Upper 
+    3.9247    12.974
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.3202      5.9279    9.0395
+
+
diff --git a/analysis/matlab/results/phase_mergeB2.txt b/analysis/matlab/results/phase_mergeB2.txt
new file mode 100644
index 0000000..ae4b8c7
--- /dev/null
+++ b/analysis/matlab/results/phase_mergeB2.txt
@@ -0,0 +1,155 @@
+==============================================================================
+PHASED days x tDCS LME -- mergeB2 (Box-B2 vs Box-A2)
+==============================================================================
+model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
+
+phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
+--------------------------------------------------------------------------------------------
+0-5      8    9.86   14.65  2.9e-08   0.399        0.013         +4.79 [+1.06, +8.53]
+6-10     7    2.40    4.80  0.18      0.142        0.253         +2.40 [-1.80, +6.60]
+6-13     7    1.71    4.42  0.083     0.127        0.018         +2.71 [+0.50, +4.93]
+
+Note: the interaction p (and CI) use fitlme observation-level DF and are
+ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
+phase carries the Box-B2 faster-acquisition signal; late phases converge.
+==============================================================================
+FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              48
+    Fixed effects coefficients           4
+    Random effects coefficients          8
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    383.06    394.29    -185.53          371.06  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}         12.524     5.5254      2.2666    44      0.028385
+    {'dayp'       }         9.8571     1.4654      6.7268    44    2.8783e-08
+    {'tDCS'       }        -5.9524     6.9892    -0.85166    44       0.39902
+    {'dayp:tDCS'  }         4.7943     1.8536      2.5865    44      0.013079
+
+
+    Lower      Upper 
+     1.3881     23.66
+     6.9039     12.81
+    -20.038    8.1334
+     1.0587    8.5299
+
+Random effects covariance parameters (95% CIs):
+Group: subject (8 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7044  
+
+
+    Lower     Upper 
+    2.6066    12.484
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.618      8.5282    13.219
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              35
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    264.22    273.56    -126.11          252.22  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}         74.2       5.8956    12.586    31    1.0133e-13
+    {'dayp'       }          2.4       1.7415    1.3781    31       0.17803
+    {'tDCS'       }        10.52       6.9757    1.5081    31       0.14166
+    {'dayp:tDCS'  }          2.4       2.0606    1.1647    31       0.25302
+
+
+    Lower      Upper 
+     62.176    86.224
+    -1.1518    5.9518
+    -3.7071    24.747
+    -1.8026    6.6026
+
+Random effects covariance parameters (95% CIs):
+Group: subject (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        5.7551  
+
+
+    Lower     Upper 
+    2.7955    11.848
+
+Group: Error
+    Name               Estimate    Lower     Upper
+    {'Res Std'}        7.7883      5.9937    10.12
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              50
+    Fixed effects coefficients           4
+    Random effects coefficients          7
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    362.57    374.04    -175.28          350.57  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}        75.168       5.6188    13.378    46    1.8212e-17
+    {'dayp'       }         1.712      0.96716    1.7701    46      0.083338
+    {'tDCS'       }        10.291       6.6299    1.5522    46       0.12747
+    {'dayp:tDCS'  }        2.7116       1.1009    2.4631    46      0.017575
+
+
+    Lower       Upper 
+      63.858    86.478
+    -0.23482    3.6588
+     -3.0544    23.636
+     0.49565    4.9275
+
+Random effects covariance parameters (95% CIs):
+Group: subject (7 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        6.379   
+
+
+    Lower     Upper 
+    3.4793    11.695
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.0508      5.7111    8.7047
+
+
diff --git a/analysis/matlab/results/phase_mergeNaive.txt b/analysis/matlab/results/phase_mergeNaive.txt
new file mode 100644
index 0000000..ab25f10
--- /dev/null
+++ b/analysis/matlab/results/phase_mergeNaive.txt
@@ -0,0 +1,155 @@
+==============================================================================
+PHASED days x tDCS LME -- mergeNaive (Box-B2 vs Box-A2)
+==============================================================================
+model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
+
+phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
+--------------------------------------------------------------------------------------------
+0-5      11    8.26   15.16  1.6e-10   0.852        0.000         +6.90 [+3.40, +10.41]
+6-10     10    5.97    4.83  3.9e-05   0.007        0.584         -1.14 [-5.31, +3.03]
+6-13     10    2.83    4.47  0.00071   0.012        0.196         +1.65 [-0.87, +4.17]
+
+Note: the interaction p (and CI) use fitlme observation-level DF and are
+ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
+phase carries the Box-B2 faster-acquisition signal; late phases converge.
+==============================================================================
+FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              65
+    Fixed effects coefficients           4
+    Random effects coefficients         11
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    532.27    545.32    -260.14          520.27  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}          9.046     4.8672    1.8586    61      0.067913
+    {'dayp'       }         8.2602     1.0773    7.6677    61    1.6422e-10
+    {'tDCS'       }        -1.4984     7.9706    -0.188    61        0.8515
+    {'dayp:tDCS'  }         6.9041     1.7525    3.9397    61    0.00021248
+
+
+    Lower       Upper 
+    -0.68649    18.779
+       6.106    10.414
+     -17.437     14.44
+      3.3998    10.408
+
+Random effects covariance parameters (95% CIs):
+Group: subject (11 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.4501  
+
+
+    Lower     Upper 
+    5.5672    16.041
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        11.565      9.5751    13.968
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              50
+    Fixed effects coefficients           4
+    Random effects coefficients         10
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    404.12    415.59    -196.06          392.12  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}           62.1      5.345      11.618    46    2.7953e-15
+    {'dayp'       }         5.9667     1.3111      4.5509    46    3.8983e-05
+    {'tDCS'       }             24     8.4512      2.8398    46     0.0067003
+    {'dayp:tDCS'  }        -1.1417      2.073    -0.55073    46       0.58449
+
+
+    Lower      Upper 
+     51.341    72.859
+     3.3276    8.6058
+     6.9887    41.011
+    -5.3144    3.0311
+
+Random effects covariance parameters (95% CIs):
+Group: subject (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        10.466  
+
+
+    Lower    Upper 
+    6.207    17.646
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.156      8.1572    12.644
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              73
+    Fixed effects coefficients           4
+    Random effects coefficients         10
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    593.06    606.81    -290.53          581.06  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE         tStat     DF    pValue    
+    {'(Intercept)'}        66.814       4.8749    13.706    69    2.2545e-21
+    {'dayp'       }        2.8256      0.79686    3.5459    69    0.00070793
+    {'tDCS'       }        19.873       7.7041    2.5796    69      0.012026
+    {'dayp:tDCS'  }         1.648       1.2621    1.3058    69       0.19595
+
+
+    Lower       Upper 
+      57.089    76.539
+      1.2359    4.4153
+       4.504    35.242
+    -0.86972    4.1658
+
+Random effects covariance parameters (95% CIs):
+Group: subject (10 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        9.246   
+
+
+    Lower     Upper 
+    5.4692    15.631
+
+Group: Error
+    Name               Estimate    Lower    Upper 
+    {'Res Std'}        11.498      9.661    13.686
+
+
diff --git a/analysis/matlab/results/phase_unmerged.txt b/analysis/matlab/results/phase_unmerged.txt
new file mode 100644
index 0000000..c4659d1
--- /dev/null
+++ b/analysis/matlab/results/phase_unmerged.txt
@@ -0,0 +1,155 @@
+==============================================================================
+PHASED days x tDCS LME -- unmerged (Box-B2 vs Box-A2)
+==============================================================================
+model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day - phaseStart]
+
+phase     N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95% CI]
+--------------------------------------------------------------------------------------------
+0-5      6    9.86   15.22  1.6e-07   0.689        0.015         +5.36 [+1.11, +9.62]
+6-10     5    2.40    4.17  0.23      0.098        0.486         +1.77 [-3.41, +6.95]
+6-13     5    1.71    3.98  0.12      0.101        0.097         +2.27 [-0.44, +4.97]
+
+Note: the interaction p (and CI) use fitlme observation-level DF and are
+ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early
+phase carries the Box-B2 faster-acquisition signal; late phases converge.
+==============================================================================
+FULL MODEL SUMMARY -- phase 0-5: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              36
+    Fixed effects coefficients           4
+    Random effects coefficients          6
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    289.66    299.16    -138.83          277.66  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat       DF    pValue    
+    {'(Intercept)'}         12.524     5.2775      2.3731    32      0.023816
+    {'dayp'       }         9.8571     1.4772      6.6729    32    1.5706e-07
+    {'tDCS'       }        -3.0159     7.4635    -0.40408    32       0.68884
+    {'dayp:tDCS'  }         5.3619      2.089      2.5667    32      0.015149
+
+
+    Lower      Upper 
+     1.7739    23.274
+     6.8482    12.866
+    -18.219    12.187
+     1.1067    9.6172
+
+Random effects covariance parameters (95% CIs):
+Group: subject (6 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        4.8527  
+
+
+    Lower     Upper 
+    1.7069    13.796
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        10.703      8.3104    13.785
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-10: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              25
+    Fixed effects coefficients           4
+    Random effects coefficients          5
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    196.97    204.29    -92.486          184.97  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat      DF    pValue    
+    {'(Intercept)'}          74.2      6.4017     11.591    21    1.3774e-10
+    {'dayp'       }           2.4      1.9295     1.2439    21       0.22726
+    {'tDCS'       }        14.333      8.2646     1.7343    21      0.097522
+    {'dayp:tDCS'  }        1.7667       2.491    0.70923    21       0.48598
+
+
+    Lower      Upper 
+     60.887    87.513
+    -1.6126    6.4126
+    -2.8539    31.521
+    -3.4136    6.9469
+
+Random effects covariance parameters (95% CIs):
+Group: subject (5 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        6.1065  
+
+
+    Lower     Upper 
+    2.5428    14.665
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        8.6289      6.3295    11.764
+
+
+==============================================================================
+FULL MODEL SUMMARY -- phase 6-13: success ~ dayp*tDCS + (1|subject)
+==============================================================================
+
+Linear mixed-effects model fit by ML
+
+Model information:
+    Number of observations              34
+    Fixed effects coefficients           4
+    Random effects coefficients          5
+    Covariance parameters                2
+
+Formula:
+    success ~ 1 + dayp*tDCS + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    257.04    266.2    -122.52          245.04  
+
+Fixed effects coefficients (95% CIs):
+    Name                   Estimate    SE        tStat     DF    pValue    
+    {'(Intercept)'}         75.17      6.2311    12.064    30    4.8879e-13
+    {'dayp'       }        1.7101       1.061    1.6117    30       0.11749
+    {'tDCS'       }        13.563      8.0252      1.69    30        0.1014
+    {'dayp:tDCS'  }         2.267      1.3237    1.7126    30      0.097101
+
+
+    Lower       Upper 
+      62.445    87.896
+    -0.45682     3.877
+     -2.8271    29.952
+    -0.43635    4.9703
+
+Random effects covariance parameters (95% CIs):
+Group: subject (5 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        7.1166  
+
+
+    Lower     Upper 
+    3.4834    14.539
+
+Group: Error
+    Name               Estimate    Lower     Upper 
+    {'Res Std'}        7.7328      5.9847    9.9914
+
+
diff --git a/analysis/matlab/results/unmerged_d0_10.txt b/analysis/matlab/results/unmerged_d0_10.txt
new file mode 100644
index 0000000..5eafa8f
--- /dev/null
+++ b/analysis/matlab/results/unmerged_d0_10.txt
@@ -0,0 +1,232 @@
+==============================================================================
+tDCS GLM report -- scenario: unmerged_d0_10
+==============================================================================
+merge key: unmerged   day window: 0..10   observations: 126
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            3         33          70.0      0.555       10
+Electrode-Box-A             1         11          58.9      0.499       10
+Electrode-Box-A2            3         28          52.1      0.433       10
+Naive                       4         43          46.8      0.440       10
+Right-Electrode             1         11          63.4      0.499       10
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    301.01    323.7    -142.51          285.01  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat       DF 
+    {'(Intercept)'           }           4.2892      0.10522      40.765    119
+    {'group_Electrode-Box-A' }         -0.16759      0.20935    -0.80052    119
+    {'group_Electrode-Box-A2'}         -0.14442      0.14887    -0.97013    119
+    {'group_Naive'           }         -0.44336      0.13882     -3.1938    119
+    {'group_Right-Electrode' }        -0.094696      0.20909    -0.45289    119
+    {'day_c'                 }          0.20188    0.0052727      38.287    119
+    {'day_c2'                }        -0.024069    0.0015714     -15.317    119
+
+
+    pValue        Lower       Upper    
+    9.1655e-72      4.0809       4.4976
+         0.425    -0.58213      0.24695
+       0.33395    -0.43919      0.15035
+     0.0017973    -0.71824     -0.16848
+       0.65145    -0.50872      0.31933
+    9.4346e-69     0.19144      0.21232
+    6.4425e-30    -0.02718    -0.020957
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17716 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             124
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC      LogLikelihood    Deviance
+    366.74    389.3    -175.37          350.74  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate     SE           tStat       DF 
+    {'(Intercept)'           }          0.21772      0.13736       1.585    117
+    {'group_Electrode-Box-A' }         -0.27791       0.2726     -1.0195    117
+    {'group_Electrode-Box-A2'}         -0.37423      0.19356     -1.9334    117
+    {'group_Naive'           }         -0.64393      0.18075     -3.5625    117
+    {'group_Right-Electrode' }         -0.25889      0.27218    -0.95118    117
+    {'day_c'                 }          0.21668    0.0069264      31.283    117
+    {'day_c2'                }        -0.013737    0.0021988     -6.2475    117
+
+
+    pValue        Lower        Upper     
+       0.11567    -0.054325       0.48976
+       0.31008     -0.81779       0.26196
+        0.0556     -0.75756     0.0091015
+     0.0005325      -1.0019      -0.28596
+       0.34348     -0.79792       0.28014
+    1.1453e-58      0.20296        0.2304
+    6.9755e-09    -0.018092    -0.0093824
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.22892 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             126
+    Fixed effects coefficients          11
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    305.19    339.23    -140.6           281.19  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate     SE           tStat   
+    {'(Intercept)'                 }           4.3338      0.10278      42.165
+    {'group_Electrode-Box-A'       }         -0.23805      0.20569     -1.1574
+    {'group_Electrode-Box-A2'      }         -0.17701      0.14533      -1.218
+    {'group_Naive'                 }         -0.54258      0.13655     -3.9736
+    {'group_Right-Electrode'       }         -0.15185      0.20519    -0.74005
+    {'day_c'                       }          0.17638    0.0080904      21.802
+    {'day_c2'                      }        -0.024639    0.0015803     -15.591
+    {'group_Electrode-Box-A:day_c' }         0.044079     0.016893      2.6093
+    {'group_Electrode-Box-A2:day_c'}         0.011922     0.012896     0.92443
+    {'group_Naive:day_c'           }         0.059567     0.011689      5.0961
+    {'group_Right-Electrode:day_c' }         0.036369     0.016336      2.2263
+
+
+    DF     pValue        Lower        Upper    
+    115    8.6925e-72       4.1302       4.5374
+    115       0.24953     -0.64548      0.16937
+    115       0.22572     -0.46489      0.11086
+    115    0.00012391     -0.81305     -0.27211
+    115       0.46078      -0.5583      0.25459
+    115    1.1738e-42      0.16036      0.19241
+    115    3.8845e-30    -0.027769    -0.021509
+    115      0.010278     0.010617      0.07754
+    115        0.3572    -0.013623     0.037467
+    115    1.3744e-06     0.036414      0.08272
+    115      0.027945      0.00401     0.068729
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.17214 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=9.52, Box-A2 mean=8.96
+  Welch two-sided p=0.643, Mann-Whitney p=0.700 (nB=3, nA=3)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=115) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.16x Box-A2  (one-sided p=0.1660)  -> not supported
+  [rate/level ] Box-B2 = 1.45x Box-A2  (one-sided p=0.0266)  -> SUPPORTED
+
+Per-animal (Box-B2 n=3 vs Box-A2 n=3), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0414, Mann-Whitney one-sided (B2>A2) p=0.0500
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0702, Mann-Whitney one-sided (B2>A2) p=0.0500
+
+Classification -- is each UNKNOWN condition A2-like or B2-like?
+(ratio >1 = above that anchor; p = differs from that anchor)
+
+  Electrode-Box-A:
+    [count/level] vs Box-A2: 0.98x p=0.912   vs Box-B2: 0.85x p=0.425
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+    [rate/level ] vs Box-A2: 1.10x p=0.725   vs Box-B2: 0.76x p=0.310
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+
+  Right-Electrode:
+    [count/level] vs Box-A2: 1.05x p=0.813   vs Box-B2: 0.91x p=0.651
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+    [rate/level ] vs Box-A2: 1.12x p=0.673   vs Box-B2: 0.77x p=0.343
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+
+  NOTE: each unknown has ONLY 1 subject. 'Matches' means 'not statistically
+  distinguishable', weak evidence at n=1, not proof of equivalence.
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.
diff --git a/analysis/matlab/results/unmerged_full.txt b/analysis/matlab/results/unmerged_full.txt
new file mode 100644
index 0000000..cacb3ac
--- /dev/null
+++ b/analysis/matlab/results/unmerged_full.txt
@@ -0,0 +1,232 @@
+==============================================================================
+tDCS GLM report -- scenario: unmerged_full
+==============================================================================
+merge key: unmerged   day window: 0..26   observations: 185
+==============================================================================
+DESCRIPTIVES
+==============================================================================
+group                  n_subj n_sessions  mean_success  mean_rate  max_day
+--------------------------------------------------------------------------
+Electrode-Box-B2            3         40          77.0      0.591       14
+Electrode-Box-A             1         15          70.7      0.557       14
+Electrode-Box-A2            3         31          55.4      0.448       13
+Naive                       4         76          59.6      0.492       26
+Right-Electrode             1         23          85.8      0.627       22
+
+==============================================================================
+(A) LEVEL / COUNT -- Poisson GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    567.38    593.14    -275.69          551.38  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE            tStat      DF 
+    {'(Intercept)'           }            4.5636      0.062818     72.648    178
+    {'group_Electrode-Box-A' }          -0.16366        0.1251    -1.3082    178
+    {'group_Electrode-Box-A2'}          -0.16301      0.090874    -1.7939    178
+    {'group_Naive'           }          -0.41017      0.083133    -4.9339    178
+    {'group_Right-Electrode' }          -0.15781       0.12382    -1.2745    178
+    {'day_c'                 }           0.10084     0.0023114     43.629    178
+    {'day_c2'                }        -0.0064669    0.00024857    -26.016    178
+
+
+    pValue         Lower         Upper     
+    3.0937e-134        4.4397        4.6876
+        0.19249      -0.41052      0.083214
+       0.074534      -0.34234      0.016314
+     1.8418e-06      -0.57422      -0.24612
+        0.20416      -0.40216      0.086543
+     5.5938e-97      0.096281        0.1054
+     1.4969e-62    -0.0069574    -0.0059764
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.10379 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             183
+    Fixed effects coefficients           7
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Binomial
+    Link                            Logit 
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + group + day_c + day_c2 + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    664.34    690.01    -324.17          648.34  
+
+Fixed effects coefficients (95% CIs):
+    Name                              Estimate      SE           tStat       DF 
+    {'(Intercept)'           }           0.67246      0.11921      5.6411    176
+    {'group_Electrode-Box-A' }           -0.2656      0.23687     -1.1213    176
+    {'group_Electrode-Box-A2'}          -0.42592      0.16928     -2.5161    176
+    {'group_Naive'           }          -0.69483      0.15707     -4.4236    176
+    {'group_Right-Electrode' }          -0.19395      0.23547    -0.82369    176
+    {'day_c'                 }           0.12539    0.0034255      36.605    176
+    {'day_c2'                }        -0.0069542    0.0003534     -19.678    176
+
+
+    pValue        Lower         Upper     
+    6.6327e-08        0.4372       0.90772
+       0.26369      -0.73306       0.20187
+      0.012762      -0.75999     -0.091839
+    1.6969e-05       -1.0048      -0.38484
+       0.41123      -0.65866       0.27075
+    3.2383e-84       0.11863       0.13215
+    2.5332e-46    -0.0076517    -0.0062568
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.19922 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+==============================================================================
+(C) LEARNING RATE -- Poisson GLMM (group x day interaction)
+==============================================================================
+
+Generalized linear mixed-effects model fit by PL
+
+Model information:
+    Number of observations             185
+    Fixed effects coefficients          11
+    Random effects coefficients         12
+    Covariance parameters                1
+    Distribution                    Poisson
+    Link                            Log   
+    FitMethod                       MPL   
+
+Formula:
+    success ~ 1 + day_c2 + group*day_c + (1 | subject)
+
+Model fit statistics:
+    AIC       BIC       LogLikelihood    Deviance
+    566.35    604.99    -271.17          542.35  
+
+Fixed effects coefficients (95% CIs):
+    Name                                    Estimate      SE            tStat   
+    {'(Intercept)'                 }            4.5639      0.066952      68.167
+    {'group_Electrode-Box-A'       }           -0.1538       0.13339     -1.1531
+    {'group_Electrode-Box-A2'      }          -0.16353      0.097941     -1.6697
+    {'group_Naive'                 }           -0.4286      0.088752     -4.8292
+    {'group_Right-Electrode'       }          -0.10141       0.13294    -0.76285
+    {'day_c'                       }          0.084832     0.0050395      16.833
+    {'day_c2'                      }        -0.0071091    0.00030673     -23.177
+    {'group_Electrode-Box-A:day_c' }          0.015434     0.0094241      1.6377
+    {'group_Electrode-Box-A2:day_c'}          0.010314     0.0089464      1.1529
+    {'group_Naive:day_c'           }          0.031147     0.0065742      4.7377
+    {'group_Right-Electrode:day_c' }          0.011613      0.007034       1.651
+
+
+    DF     pValue         Lower         Upper     
+    174    1.9324e-127        4.4318        4.6961
+    174        0.25047      -0.41707       0.10946
+    174       0.096774      -0.35684      0.029772
+    174     2.9893e-06      -0.60377      -0.25343
+    174        0.44659       -0.3638       0.16097
+    174     2.3384e-38      0.074885      0.094778
+    174     4.4285e-55    -0.0077145    -0.0065037
+    174        0.10329    -0.0031665      0.034034
+    174        0.25055    -0.0073435      0.027971
+    174     4.4703e-06      0.018171      0.044122
+    174        0.10054    -0.0022698      0.025496
+
+Random effects covariance parameters:
+Group: subject (12 Levels)
+    Name1                  Name2                  Type           Estimate
+    {'(Intercept)'}        {'(Intercept)'}        {'std'}        0.11116 
+
+Group: Error
+    Name                        Estimate
+    {'sqrt(Dispersion)'}        1       
+
+
+Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:
+Per-subject OLS slope of success vs day: Box-B2 mean=7.96, Box-A2 mean=8.19
+  Welch two-sided p=0.896, Mann-Whitney p=1.000 (nB=3, nA=3)  -> parallel learning (no slope difference detected)
+(Reference only: the GLMM group x day_c joint F-test gives p=0.0000, but with just
+ observation-level DF (df2=174) it is ANTICONSERVATIVE for this few-subject design and is
+ NOT the basis for the conclusion above.)
+
+==============================================================================
+INTERPRETATION
+==============================================================================
+Note: these are subject-level GLMMs (Laplace-approximated fitglme), not the
+Python reference's population-average GEE -- directions/magnitudes should agree,
+exact ratios and p-values will differ.
+
+Anchor check -- H2: Box-B2 BETTER than Box-A2 (the two anchors must differ)
+  [count/level] Box-B2 = 1.18x Box-A2  (one-sided p=0.0364)  -> SUPPORTED
+  [rate/level ] Box-B2 = 1.53x Box-A2  (one-sided p=0.0059)  -> SUPPORTED
+
+Per-animal (Box-B2 n=3 vs Box-A2 n=3), pure stats (no GLME):
+  count (per-subject mean success): Welch two-sided p=0.0552, Mann-Whitney one-sided (B2>A2) p=0.0500
+  rate  (per-subject pooled success/total): Welch two-sided p=0.0722, Mann-Whitney one-sided (B2>A2) p=0.0500
+
+Classification -- is each UNKNOWN condition A2-like or B2-like?
+(ratio >1 = above that anchor; p = differs from that anchor)
+
+  Electrode-Box-A:
+    [count/level] vs Box-A2: 1.00x p=0.996   vs Box-B2: 0.85x p=0.192
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+    [rate/level ] vs Box-A2: 1.17x p=0.501   vs Box-B2: 0.77x p=0.264
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+
+  Right-Electrode:
+    [count/level] vs Box-A2: 1.01x p=0.967   vs Box-B2: 0.85x p=0.204
+                 -> AMBIGUOUS (nearer Electrode-Box-A2)
+    [rate/level ] vs Box-A2: 1.26x p=0.328   vs Box-B2: 0.82x p=0.411
+                 -> AMBIGUOUS (nearer Electrode-Box-B2)
+
+  NOTE: each unknown has ONLY 1 subject. 'Matches' means 'not statistically
+  distinguishable', weak evidence at n=1, not proof of equivalence.
+
+==============================================================================
+CAVEATS
+==============================================================================
+  - Tiny groups: each arm has only n=3-4 subjects (Naive n=4; anchor arms n=3-5
+    depending on merge), and -- in unmerged scenarios -- each unknown condition
+    (Electrode-Box-A, Right-Electrode) has only n=1 subject. Treat every group
+    comparison here as preliminary.
+  - Single-subject classification: for a 1-subject unknown, 'matches anchor X'
+    means 'not statistically distinguishable from X', NOT proof of equivalence;
+    inference with a single subject in a group is fragile.
+  - Count vs rate: 'success' alone is a raw count; the rate model
+    (success/attempts) is the fairer accuracy comparison when attempt counts differ
+    between groups.
diff --git a/analysis/matlab/tdcs_lme_report.m b/analysis/matlab/tdcs_lme_report.m
index 1a0e993..9b8a1bd 100644
--- a/analysis/matlab/tdcs_lme_report.m
+++ b/analysis/matlab/tdcs_lme_report.m
@@ -23,6 +23,8 @@ if abs(L.maxDayB - L.maxDayA) > 2
 end
 s = [s sprintf('\n')];
 
+s = [s tdcs_model_summary(L.lme, 'fitlme: success ~ day*tDCS + (1|subject)') sprintf('\n')];
+
 s = [s sprintf('%-27s %-14s %-13s %s\n', 'effect', 't (df)', 'F (df1)', 'p')];
 s = [s sprintf('%s\n', repmat('-', 1, 70))];
 s = [s localRow('days x tDCS (interaction)', L.interaction)];
diff --git a/analysis/matlab/tdcs_model_summary.m b/analysis/matlab/tdcs_model_summary.m
new file mode 100644
index 0000000..3631860
--- /dev/null
+++ b/analysis/matlab/tdcs_model_summary.m
@@ -0,0 +1,28 @@
+function s = tdcs_model_summary(model, label)
+%TDCS_MODEL_SUMMARY Full native fitlme/fitglme model summary as plain text.
+%   S = TDCS_MODEL_SUMMARY(MODEL) captures DISP(MODEL) -- the complete summary
+%   MATLAB prints for a fitted LinearMixedModel / GeneralizedLinearMixedModel
+%   (model information and fit statistics, the formula, the fixed-effects
+%   coefficient table with 95% CIs, and the random-effects covariance) -- and
+%   strips the Command-Window-only <strong>...</strong> bold markup that EVALC
+%   would otherwise emit as literal text, so the result is plain text suitable
+%   for the console and the results/*.txt files.
+%
+%   S = TDCS_MODEL_SUMMARY(MODEL, LABEL) prefixes a titled banner naming the
+%   model (typically its formula), so each scenario's output carries a
+%   self-describing copy of the raw GLM/LME summary.
+%
+%   This is the single place that renders a fitted model verbatim; every report
+%   (tdcs_report, tdcs_lme_report, tdcs_paper_lme, tdcs_phase_lme) routes
+%   through it so that ALL scenarios print the full summary generated by the
+%   MATLAB model-fitting functions.
+
+raw = evalc('disp(model)');
+s = regexprep(raw, '</?strong>', '');
+
+if nargin > 1 && ~isempty(label)
+    bar = repmat('=', 1, 78);
+    s = sprintf('%s\nFULL MODEL SUMMARY -- %s\n%s\n%s', bar, label, bar, s);
+end
+
+end
diff --git a/analysis/matlab/tdcs_paper_lme.m b/analysis/matlab/tdcs_paper_lme.m
index da5bbd8..a697e99 100644
--- a/analysis/matlab/tdcs_paper_lme.m
+++ b/analysis/matlab/tdcs_paper_lme.m
@@ -67,6 +67,8 @@ if abs(L.maxDayStim - L.maxDayCtrl) > 2
         '   extrapolates the control group''s line and is CONFOUNDED here (the paper''s\n' ...
         '   groups had equal coverage). See the _d0_13 fair-window and phased analyses. **\n'])];
 end
+s = [s sprintf('\n')];
+s = [s tdcs_model_summary(L.model, 'fitlme: behavior ~ stim + day + stim:day + (1|rat)') sprintf('\n')];
 s = [s sprintf('\n%-26s %-13s %-13s %s\n', 'effect', 't (df)', 'F (df1)', 'p')];
 s = [s sprintf('%s\n', repmat('-', 1, 66))];
 s = [s localRow('stim x day (interaction)', L.interaction)];
diff --git a/analysis/matlab/tdcs_phase_lme.m b/analysis/matlab/tdcs_phase_lme.m
index 4e08910..3eb66f0 100644
--- a/analysis/matlab/tdcs_phase_lme.m
+++ b/analysis/matlab/tdcs_phase_lme.m
@@ -33,6 +33,7 @@ s = [s sprintf('model per phase: success ~ dayp*tDCS + (1|subject)  [dayp = day
 s = [s sprintf('%-9s N  A2slope B2slope  day p     tDCS(lvl) p  interaction p  slopeDiff [95%% CI]\n', 'phase')];
 s = [s sprintf('%s\n', repmat('-', 1, 92))];
 
+rawSummaries = '';
 for k = 1:numel(phases)
     ph = phases{k};
     Tp = T(T.day >= ph(1) & T.day <= ph(2), :);
@@ -59,12 +60,18 @@ for k = 1:numel(phases)
     s = [s sprintf('%d-%-6d %d  %6.2f  %6.2f  %-9.2g %-11.3f  %-13.3f %+.2f [%+.2f, %+.2f]\n', ...
         ph(1), ph(2), e.nSub, e.a2Slope, e.b2Slope, e.dayP, e.tDCSlevelP, ...
         e.interP, e.interEst, e.interCI(1), e.interCI(2))]; %#ok<AGROW>
+
+    rawSummaries = [rawSummaries tdcs_model_summary(lme, ...
+        sprintf('phase %d-%d: success ~ dayp*tDCS + (1|subject)', ph(1), ph(2))) ...
+        sprintf('\n')]; %#ok<AGROW>
 end
 
 s = [s sprintf(['\nNote: the interaction p (and CI) use fitlme observation-level DF and are\n' ...
     'ANTICONSERVATIVE at these small subject counts (see tdcs_power_sim). The early\n' ...
     'phase carries the Box-B2 faster-acquisition signal; late phases converge.\n'])];
 
+s = [s rawSummaries];
+
 fprintf('%s', s);
 localWrite(['phase_' mergeKey], s);
 
diff --git a/analysis/matlab/tdcs_report.m b/analysis/matlab/tdcs_report.m
index 066864d..008dd77 100644
--- a/analysis/matlab/tdcs_report.m
+++ b/analysis/matlab/tdcs_report.m
@@ -87,7 +87,9 @@ s = [s localCleanDisp(R.countGLME) sprintf('\n')];
 s = [s localHeader('(B) LEVEL / RATE -- Binomial GLMM (subject random intercept)')];
 s = [s localCleanDisp(R.rateGLME) sprintf('\n')];
 
-s = [s localHeader('(C) LEARNING RATE -- per-animal slope test (Box-B2 vs Box-A2)')];
+s = [s localHeader('(C) LEARNING RATE -- Poisson GLMM (group x day interaction)')];
+s = [s localCleanDisp(R.learnGLME) sprintf('\n')];
+s = [s sprintf('Per-animal OLS slope test (Box-B2 vs Box-A2), cluster-honest:\n')];
 if R.learn.slopeMWUp < 0.05
     verdict = 'DIFFERENT learning rates';
 else
@@ -103,12 +105,11 @@ s = [s sprintf(['(Reference only: the GLMM group x day_c joint F-test gives p=%.
 end
 
 function s = localCleanDisp(model)
-%LOCALCLEANDISP disp(MODEL) captured to text, with the Command-Window-only
-%   <strong>...</strong> bold-markup tags (rendered by the interactive
-%   MATLAB terminal, but emitted as literal text by EVALC) stripped so the
-%   report/console/file output is plain text.
-raw = evalc('disp(model)');
-s = regexprep(raw, '</?strong>', '');
+%LOCALCLEANDISP Full native model summary (disp(MODEL)) as plain text.
+%   Thin wrapper over the shared TDCS_MODEL_SUMMARY so the count, rate, and
+%   learning GLMMs here render the same verbatim summary used by the LME
+%   reports.
+s = tdcs_model_summary(model);
 end
 
 % ------------------------------------------------------------ interpretation
diff --git a/analysis/matlab/tests/tLme.m b/analysis/matlab/tests/tLme.m
index cc2db21..d3e0d7a 100644
--- a/analysis/matlab/tests/tLme.m
+++ b/analysis/matlab/tests/tLme.m
@@ -59,6 +59,28 @@ classdef tLme < matlab.unittest.TestCase
             testCase.verifyError(@() tdcs_glm('lme_nonsense'), 'tdcs_glm:badScenario');
         end
 
+        function testReportsIncludeFullModelSummary(testCase)
+            % Every scenario prints the full native fitlme summary. The lme_*,
+            % paper_*, and phase_* reports each embed disp(model), whose
+            % 'Fixed effects coefficients' header is the marker (phase_* has
+            % one per phase -> 3). Markup must be stripped.
+            here = fileparts(fileparts(mfilename('fullpath')));   % analysis/matlab
+            resDir = fullfile(here, 'results');
+            cases = struct( ...
+                'lme_mergeA2_full', 1, ...
+                'paper_mergeA2',    1, ...
+                'phase_mergeA2',    3);
+            for sc = fieldnames(cases)'
+                name = sc{1};
+                evalc(sprintf('tdcs_glm(''%s'')', name));
+                txt = fileread(fullfile(resDir, [name '.txt']));
+                testCase.verifyEqual(count(txt, 'Fixed effects coefficients (95% CIs):'), ...
+                    cases.(name), sprintf('%s: missing full model summary', name));
+                testCase.verifyFalse(contains(txt, '<strong>'), ...
+                    sprintf('%s: markup not stripped', name));
+            end
+        end
+
         function testMergeNaiveGrouping(testCase)
             % mergeNaive: control = Box-A2 + Naive (7), tDCS = Box-B2 + Right (4),
             % Box-A unchanged (1); Naive is relabeled away.
diff --git a/analysis/matlab/tests/tReport.m b/analysis/matlab/tests/tReport.m
index 51ca31e..6daba27 100644
--- a/analysis/matlab/tests/tReport.m
+++ b/analysis/matlab/tests/tReport.m
@@ -28,6 +28,10 @@ classdef tReport < matlab.unittest.TestCase
             testCase.verifyTrue(contains(txt, 'CAVEATS'));
             % Command-Window-only markup must be stripped from the report.
             testCase.verifyFalse(contains(txt, '<strong>'));
+            % Full native GLMM summary is present for all three models
+            % (count, rate, learning-rate): each disp() prints this header.
+            testCase.verifyEqual(count(txt, 'Fixed effects coefficients (95% CIs):'), 3, ...
+                'Expected the full fitglme summary for count, rate, and learning GLMMs.');
         end
 
         function testUnmergedScenarioIncludesClassification(testCase)