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DataDictionary.txt

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+%%%%%% Demographics 'DeID_Dems.csv' %%%%%%
+subno - Decoded Subject ID Number
+group - Diagnosis
+Age - Age
+Cloz - Clozapine Status
+Race - Race
+Educat_Level - Education
+Sex - Sex
+WASI2VerbSumIQ - Wechsler Abbreviated Scale of Intelligence Verbal IQ
+WASI2PerfSumIQ - Performance IQ
+WASI2_FullEstimatedIQ - Estimated Full-Scale IQ
+WTAR_RAW - Wechsler Test of Adult Reading Raw values
+WTAR_SS - WTAR Scaled values to estimated premorbid IQ
+BPAGD - BPRS Axiety, Guilt Depression
+BPNeg - BPRS Negative Symptoms
+BPRD - BPRS Reality Distortion
+BPtot - BPRS Total Symptom Score
+
+%%%%%% Behavioural data %%%%%%
+%% Training
+Column 1 - Unused
+Column 2 - Trial
+Column 3 - Stimulus Type (A=1; B=2; C=3; D=4)
+Column 4 - Stimulus Colour (yellow=1; blue=2)
+Column 5 - Stimulus Response Congruency (congruent=1; incongruent=2)
+Column 6 - Probability of Reward for correct outcome (yes = 1; no = 0)
+Column 7 - Response (No response = -1; Response = 1)
+Column 8 - Appropriate Response (Too slow/Error = 0; Correct = 1)
+Column 9 - Response Time (s)
+Column 10 - Accuracy (Too slow = -1; Incorrect = 0; Correct = 1)
+Column 11 - Feedback (Error/Too Slow = -3; 0; +1)
+
+% Stimulus Type
+A=1; B=2; C=3; D=4
+A = Reinforced 100%
+B = Reinforced 100% on congruent
+C = Reinforced 100% on incongruent
+D = Reinforced 0%
+
+% Stimulus Colour (determines which button to press - L/R randomised)
+yellow=1; blue=2;
+
+% Stimulus Congruency
+congruent=1; incongruent=2;
+
+%% Test/Transfer
+Column 1 - Unused
+Column 2 - Trial
+Column 3 - Stimulus Type pairs (A=1; B=2; C=3; D=4) in LeftRight format
+Column 4 - Response ( =1)
+Column 5 - Selected Stimulus (Left = 5; Right = 6)
+Column 6 - Response Time (ms)
+Column 7 - Accuracy (Suboptimal = 0; Optimal = 1)
+
+%%%%%% Triggers %%%%%%
+%% Stimulus Triggers
+Stimulus onset trigger format = Colour:Congruency:Type
+e.g., 124 = yellow:incongruent:D
+
+%% Response Triggers
+Press Left Correct = 101; Press Right Correct = 102
+Press Left Incorrect = 103; Press Right Incorrect = 104
+
+%% Feedback Triggers
+-3 Error! = 6
+-3! No Response = 7
+Correct +1 = 8
+No Reward 0 = 9
+
+%% Test/Transfer Triggers
+% Stimulus Triggers
+Transfer stimulus onset trigger format = Stim 1:Stim 2
+e.g., 23 = B vs C
+
+% Response Triggers
+(nb optimal is A > B > C > D - but B == C)
+Optimal Response Left 1 = 1: Optimal Response Right 2 = 2
+Suboptimal Left = 3: Suboptimal Right = 4

DeID_Dems.csv

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+subno,group,Age,Cloz,Race,Educat_Level,Sex,WASI2VerbSumIQ,WASI2PerfSumIQ,WASI2_FullEstimatedIQ,WTAR_RAW,WTAR_SS,BPAGD,BPNeg,BPRD,BPDis,BPtot
+101,SZ,55.5,Yes,White,16,Female,101,107,105,38,108,4,6,7,3,28
+102,SZ,41.7,Yes,Black,12,Male,87,70,76,30,95,5,6,8,3,31
+103,SZ,45.9,Yes,White,7,Female,93,92,91,24,86,4,6,6,3,29
+104,SZ,51.5,No,White,12,Female,91,101,96,20,80,3,6,6,3,26
+105,SZ,35.8,No,Black,12,Male,88,97,91,20,80,6,8,18,3,48
+181,SZ,44.1,Yes,White,15,Male,112,96,105,45,117,6,7,7,3,30
+107,SZ,42.6,No,White,15,Male,110,104,108,45,117,3,8,4,3,30
+108,SZ,31.9,No,Black,13,Female,107,75,89,36,105,3,5,4,3,22
+109,SZ,42.3,No,Black,15,Male,95,95,94,24,86,3,6,4,3,25
+110,SZ,49.4,Yes,White,9,Male,88,69,76,20,80,5,4,6,3,27
+111,SZ,42,Yes,Black,15,Male,93,87,88,27,90,4,5,8,3,29
+112,SZ,37.1,No,White,13,Male,104,107,106,26,89,5,4,7,3,28
+113,SZ,52.5,Yes,White,12,Male,91,98,94,18,77,6,4,12,3,36
+114,SZ,43.9,Yes,Black,12,Male,85,79,80,23,84,3,11,4,3,32
+115,SZ,55.3,No,White,12,Female,91,79,83,19,80,4,7,14,3,42
+116,SZ,34.7,Yes,Black,9,Male,92,87,88,9,62,4,5,6,3,28
+117,SZ,52.4,No,White,14,Female,101,108,105,48,122,3,8,8,3,32
+118,SZ,34.8,Yes,White,16,Male,99,123,112,47,122,10,10,4,3,37
+119,SZ,32.7,Yes,Black,12,Male,85,91,86,22,83,7,7,11,3,37
+120,SZ,48.2,No,Black,12,Male,89,81,83,20,80,9,9,18,3,52
+149,HC,50.4,HC,White,16,Male,121,103,114,50,125,NA,NA,NA,NA,NA
+121,SZ,29.7,Yes,Other Race,11,Male,106,59,107,41,113,6,7,9,3,35
+122,SZ,28.4,Yes,White,18,Male,139,125,136,48,124,3,6,4,3,23
+150,HC,49.3,HC,White,16,Male,116,120,120,39,108,NA,NA,NA,NA,NA
+123,SZ,33.6,Yes,White,13,Female,103,101,103,31,97,5,4,4,3,23
+124,SZ,29.2,Yes,White,16,Male,110,110,111,48,124,7,5,8,3,32
+125,SZ,28.1,Yes,White,13,Male,116,108,114,48,124,6,5,4,3,30
+126,SZ,52.9,No,Black,11,Female,97,81,87,23,84,3,3,4,3,22
+127,SZ,36.7,Yes,Black,16,Male,103,99,101,32,98,3,5,8,3,27
+128,SZ,46.4,Yes,White,NA,Male,87,80,81,29,93,4,9,4,4,34
+129,SZ,27.4,No,White,12,Female,98,96,96,15,72,3,4,4,3,23
+130,SZ,27.2,Yes,White,15,Male,109,120,116,36,105,6,5,5,3,31
+131,SZ,27.3,Yes,White,15,Male,113,120,119,49,125,4,5,4,3,24
+132,SZ,38.9,No,White,16,Female,94,76,83,48,122,10,10,4,3,36
+133,SZ,35,No,White,14,Female,86,69,75,35,103,4,5,11,3,32
+151,HC,29.6,HC,White,12,Male,122,120,124,43,116,NA,NA,NA,NA,NA
+134,SZ,41.9,Yes,White,16,Male,117,93,106,46,119,16,5,19,3,58
+135,SZ,27.2,Yes,Black,14,Male,88,85,84,28,92,3,15,5,3,38
+136,SZ,25.5,No,Black,12,Female,105,87,95,43,116,4,8,7,3,34
+152,HC,29.3,HC,White,16,Male,119,119,122,48,124,NA,NA,NA,NA,NA
+153,HC,42.6,HC,Black,16,Male,104,100,102,47,120,NA,NA,NA,NA,NA
+154,HC,40.5,HC,Black,19,Male,125,93,111,49,123,NA,NA,NA,NA,NA
+137,SZ,22.8,No,Black,14,Male,108,113,112,28,92,7,7,12,4,46
+155,HC,48.2,HC,White,15,Male,112,133,124,47,120,NA,NA,NA,NA,NA
+138,SZ,50.4,No,White,12,Female,95,70,79,23,84,3,4,4,3,22
+156,HC,38.1,HC,White,16,Male,130,129,134,48,122,NA,NA,NA,NA,NA
+139,SZ,28.8,No,Black,16,Female,102,92,97,42,114,3,7,4,3,24
+140,SZ,45.9,No,White,7,Female,82,86,82,22,83,3,5,22,3,46
+157,HC,42.8,HC,White,18,Female,123,118,124,48,122,NA,NA,NA,NA,NA
+158,HC,46,HC,White,14,Male,103,96,100,28,92,NA,NA,NA,NA,NA
+159,HC,27.5,HC,Other Race,16,Female,113,105,110,42,114,NA,NA,NA,NA,NA
+141,SZ,30.8,Yes,Asian,16,Male,113,116,116,42,114,3,7,11,3,35
+142,SZ,31.2,No,Black,11,Female,98,81,88,13,68,15,9,8,3,50
+160,HC,45.2,HC,White,12,Male,98,91,93,40,110,NA,NA,NA,NA,NA
+143,SZ,43.2,No,Black,13,Female,82,69,73,17,75,3,6,4,3,28
+161,HC,32.7,HC,White,18,Male,114,141,130,49,125,NA,NA,NA,NA,NA
+144,SZ,30,No,Black,12,Male,55,55,55,18,76,6,5,6,3,30
+162,HC,46.8,HC,Black,15,Female,114,98,107,48,122,NA,NA,NA,NA,NA
+163,HC,32,HC,Black,13,Female,102,65,82,30,95,NA,NA,NA,NA,NA
+164,HC,21.3,HC,Other Race,13,Female,107,101,105,33,100,NA,NA,NA,NA,NA
+145,SZ,40.2,No,White,12,Male,107,108,108,35,102,6,5,4,3,28
+165,HC,32.9,HC,White,12,Female,90,81,83,22,83,NA,NA,NA,NA,NA
+166,HC,44.9,HC,Black,16,Male,126,108,120,49,123,NA,NA,NA,NA,NA
+167,HC,47.8,HC,White,13,Male,103,94,99,44,116,NA,NA,NA,NA,NA
+168,HC,28.8,HC,White,20,Male,151,105,130,50,127,NA,NA,NA,NA,NA
+169,HC,23.6,HC,Black,13,Male,116,110,115,44,118,NA,NA,NA,NA,NA
+170,HC,46.8,HC,Black,12,Female,103,85,93,19,78,NA,NA,NA,NA,NA
+171,HC,33.2,HC,Black,17,Female,99,92,95,21,81,NA,NA,NA,NA,NA
+172,HC,49,HC,White,12,Female,102,118,111,35,102,NA,NA,NA,NA,NA
+173,HC,20.5,HC,Other Race,15,Female,130,107,122,46,122,NA,NA,NA,NA,NA
+174,HC,52.9,HC,Black,16,Male,106,101,105,38,107,NA,NA,NA,NA,NA
+175,HC,23.6,HC,Black,16,Male,118,116,120,34,102,NA,NA,NA,NA,NA
+176,HC,52.7,HC,Black,14,Female,99,91,94,27,90,NA,NA,NA,NA,NA
+177,HC,30.3,HC,White,13,Male,127,105,119,36,105,NA,NA,NA,NA,NA
+146,SZ,31.2,Yes,White,NA,Male,95,122,108,43,116,6,10,16,3,47
+178,HC,28.6,HC,Black,16,Male,122,99,112,40,111,NA,NA,NA,NA,NA
+147,SZ,39.6,No,White,16,Male,122,121,125,50,125,4,3,5,3,24
+179,HC,29.1,HC,White,17,Male,105,115,111,43,116,NA,NA,NA,NA,NA
+180,HC,21.5,HC,White,15,Male,104,120,113,42,115,NA,NA,NA,NA,NA

LICENSE.txt

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MMN_ElectrodeCoords_MA2.ced

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+Number	labels	theta	radius	X	Y	Z	sph_theta	sph_phi	sph_radius	type	
+1	Fp1	-18	0.5	0.951	0.309	  0	 18	  0	  1		
+2	Fz	 -0	0.25	0.707	  0	0.707	  0	 45	  1		
+3	F3	-39.3	0.334	0.672	0.55	0.497	39.3	29.8	  1		
+4	F7	-54	0.5	0.588	0.809	  0	 54	  0	  1		
+5	AFp9	-26.3	0.648	0.801	0.396	-0.449	26.3	-26.7	  1		
+6	FC5	-68.9	0.384	0.337	0.872	0.355	68.9	20.8	  1		
+7	FC1	-43.6	0.174	0.377	0.359	0.854	43.6	58.6	  1		
+8	C3	-90	0.25	4.33e-17	0.707	0.707	 90	 45	  1		
+9	T7	-90	0.5	6.12e-17	  1	  0	 90	  0	  1		
+10	TP9	-108	0.665	-0.268	0.826	-0.495	108	-29.7	  1		
+11	CP5	-111	0.384	-0.335	0.873	0.355	111	20.8	  1		
+12	CP1	-136	0.174	-0.375	0.362	0.854	136	58.6	  1		
+13	Pz	180	0.25	-0.707	-8.66e-17	0.707	-180	 45	  1		
+14	P3	-141	0.334	-0.674	0.546	0.497	141	29.8	  1		
+15	P7	-126	0.5	-0.588	0.809	  0	126	  0	  1		
+16	O1	-162	0.5	-0.951	0.309	  0	162	  0	  1		
+17	Oz	180	0.5	 -1	-1.22e-16	  0	-180	  0	  1		
+18	O2	162	0.5	-0.951	-0.309	  0	-162	  0	  1		
+19	P4	141	0.334	-0.674	-0.546	0.497	-141	29.8	  1		
+20	P8	126	0.5	-0.588	-0.809	  0	-126	  0	  1		
+21	TP10	108	0.665	-0.268	-0.826	-0.495	-108	-29.7	  1		
+22	CP6	111	0.384	-0.335	-0.873	0.355	-111	20.8	  1		
+23	CP2	136	0.174	-0.375	-0.362	0.854	-136	58.6	  1		
+24	Cz	 -0	  0	6.12e-17	  0	  1	  0	 90	  1		
+25	C4	 90	0.25	4.33e-17	-0.707	0.707	-90	 45	  1		
+26	T8	 90	0.5	6.12e-17	 -1	  0	-90	  0	  1		
+27	AFp10	26.3	0.648	0.802	-0.396	-0.448	-26.3	-26.6	  1		
+28	FC6	68.9	0.384	0.337	-0.872	0.355	-68.9	20.8	  1		
+29	FC2	43.6	0.174	0.377	-0.359	0.854	-43.6	58.6	  1		
+30	F4	39.3	0.334	0.672	-0.55	0.497	-39.3	29.8	  1		
+31	F8	 54	0.5	0.588	-0.809	  0	-54	  0	  1		
+32	Fp2	 18	0.5	0.951	-0.309	  0	-18	  0	  1		
+33	AF7	-36	0.5	0.809	0.588	  0	 36	  0	  1		
+34	AF3	-23.3	0.417	0.887	0.382	0.259	23.3	 15	  1		
+35	AFz	 -0	0.375	0.924	  0	0.383	  0	22.5	  1		
+36	F1	-23	0.274	0.698	0.296	0.652	 23	40.7	  1		
+37	F5	-48.9	0.413	0.633	0.726	0.269	48.9	15.6	  1		
+38	VEOGL	-16.7	0.662	0.953	0.286	-0.556	16.7	-29.2	1.14		
+39	FC3	-61.5	0.273	0.361	0.665	0.653	61.5	40.8	  1		
+40	FCz	 -0	0.125	0.383	  0	0.924	  0	67.5	  1		
+41	C1	-90	0.125	2.34e-17	0.383	0.924	 90	67.5	  1		
+42	C5	-90	0.375	5.66e-17	0.924	0.383	 90	22.5	  1		
+43	TP7	-108	0.5	-0.309	0.951	  0	108	  0	  1		
+44	CP3	-119	0.273	-0.367	0.662	0.653	119	40.8	  1		
+45	P1	-157	0.274	-0.698	0.296	0.652	157	40.7	  1		
+46	P5	-131	0.413	-0.632	0.727	0.269	131	15.6	  1		
+47	PO7	-144	0.5	-0.809	0.588	  0	144	  0	  1		
+48	PO3	-157	0.417	-0.889	0.377	0.259	157	 15	  1		
+49	POz	180	0.375	-0.924	-1.13e-16	0.383	-180	22.5	  1		
+50	PO4	157	0.417	-0.889	-0.377	0.259	-157	 15	  1		
+51	PO8	144	0.5	-0.809	-0.588	  0	-144	  0	  1		
+52	P6	131	0.413	-0.632	-0.727	0.269	-131	15.6	  1		
+53	P2	157	0.274	-0.698	-0.296	0.652	-157	40.7	  1		
+54	CPz	180	0.125	-0.383	-4.69e-17	0.924	-180	67.5	  1		
+55	CP4	119	0.273	-0.367	-0.662	0.653	-119	40.8	  1		
+56	TP8	108	0.5	-0.309	-0.951	  0	-108	  0	  1		
+57	C6	 90	0.375	5.66e-17	-0.924	0.383	-90	22.5	  1		
+58	C2	 90	0.125	2.34e-17	-0.383	0.924	-90	67.5	  1		
+59	FC4	61.5	0.273	0.361	-0.665	0.653	-61.5	40.8	  1		
+60	VEOGU	-15.6	0.564	0.951	0.265	-0.2	15.6	-11.5	1.01		
+61	F6	48.9	0.413	0.633	-0.726	0.269	-48.9	15.6	  1		
+62	F2	 23	0.274	0.698	-0.296	0.652	-23	40.7	  1		
+63	AF4	23.3	0.417	0.887	-0.382	0.259	-23.3	 15	  1		
+64	AF8	 36	0.5	0.809	-0.588	  0	-36	  0	  1		

Pre-processing_Pseudo.txt

@@ -0,0 +1,71 @@
+%% Pre-processing pipeline pseudocode
+%% Everything run in MATLAB
+%% Necessary extensions - eeglab, preppipeline, AMICA, corrmap, CSD
+
+% Preprocessing
+    read in data
+    re-reference to average
+    remove baseline
+    insert channel locations 'MMN_ElectrodeCoords_MA2.ced)'
+    High Pass Filter - 'pop_eegfiltnew( EEG, 1, [], [], [0], 0, 0, 0);'
+    Run preppipeline
+        intpChans = setxor(1:64, [5 27 38 60]); % which channels to use for interpolation; exclude EOG channels
+        params = struct('name', [vhdr{1} 'snum' num2str(sx) '.' num2str(nf)], ...
+                    'lineFrequencies', [60:60:300], ...    % Line frequencies to remove
+                    'referenceChannels', intpChans, ...    % Channels to be used for re-referencing
+                    'evaluationChannels', intpChans, ...   % Channels to be used for interpolation
+                    'rereferencedChannels', [1:64], ...    % Channels to be linenoise removed and referenced
+                    'detrendChannels', [1:64], ...         % Channels to detrend
+                    'lineNoiseChannels', [1:64], ...       % Channels to remove line noise from
+                    'ignoreBoundaryEvents', true, ...      % ??? not sure why it doesn't work without
+                    'detrendType', 'none', ...             % Use high pass filter to detrend channels
+                    'detrendCutoff', 1, ...                % High pass filter frequency cut off
+                    'referenceType', 'robust', ...         % Robust reference
+                    'meanEstimateType', 'median', ...      % Use median for robust reference
+                    'interpolationOrder', 'post-reference', ... % Interpolate channels after robust referencing
+                    'keepFiltered', true);                % Retain filtering?                   
+        EEG = prepPipeline(EEG, params);
+    Low Pass Filter - 
+        pop_eegfiltnew( EEG, [], 50, [], [0], 0, 0, 0);
+    Epoch to -1.5 - 3 seconds around stimulus using these triggers 
+        [111:114 121:124 211:214 221:224]
+    Remove large artifacts using pop_autorej 
+        pop_autorej(EEG, 'nogui','on','threshold',500, 'startprob',5,'maxrej',5);
+    Downsample to 500 Hz 
+        pop_resample( EEG, 500 );
+    Run AMICA using the following
+        [EEG.icaweights, EEG.icasphere, EEG.icamods] = runamica15(EEG.data(:,:), ...
+            'max_iter', 10000, 'writestep', 500, 'pcakeep', 42, 'do_reject', 1,  ...
+            'max_threads', 12, 'rejsig', 4);
+    Create eeglab 'STUDY' structure
+    Precompute topography to extract EOG using STUDY
+        std_precomp(STUDY, ALLEEG, 'components','allcomps','on','recompute','on','scalp','on');
+    Find prototypical VEOG artifact and run 'corrmap' to identify all VEOG components
+        corrmap(STUDY, ALLEEG, indx, 1, 'ics',1,'pl', 'both','clname','VEOG','badcomps','no', 'resetclusters','on');
+    Reject epochs by trend and component spectra, exclude for VEOG artifact
+        pop_rejtrend( EEG, 0, include, size(EEG.times,2), 5, 0.3, 0, 0, 0);
+        pop_rejspec(  EEG, 0, 'elecrange', include ,'threshold',[-50 45;-100 30],'freqlimits',[0 3;20 50],'eegplotcom','','eegplotplotallrej',0,'eegplotreject',0);
+        EEG.reject.sum = (EEG.reject.icarejfreq + EEG.reject.icarejconst) > 0;
+        pop_select( EEG, 'notrial', find(EEG.reject.sum));
+    Run second round ICA on cleaner data
+        [EEG.icaweights, EEG.icasphere, EEG.icamods] = runamica15(EEG.data(:,:), ...
+            'max_iter', 10000, 'writestep', 500, 'pcakeep', 42, 'do_reject', 1,  ...
+            'max_threads', 12, 'rejsig', 4);
+    Identify VEM, HEOG and VEOG artifacts and run corrmap again to identify
+    Subtract artifacts from EEG
+        pop_subcomp( EEG, [VEM HEOG VEOG], 0); % Check this
+    Final clean up - re-baseline correct to 100 ms epoch baseline and remove large noisy epochs
+        pop_rmbase( EEG, [-100 0]);
+        pop_eegthresh(  EEG, 1, [1:64], -90, 90, -1.5, 3, 0, 1);
+    Run CSD transformation
+        Remove eye channels
+        ConvertLocations
+        ExtractMontage for CSD
+        Check Montage (MapMontage(M))
+        Set CSD: [G,H] = GetGH(M, 4);
+        Run CSD: CSD(EEG.data(:,:), G, H, 1.0e-5, 10);
+                 reshape(EEG.CSDdata, size(EEG.data));
+
+
+
+

README.md

@@ -0,0 +1,64 @@
+---
+license: pddl
+tags:
+- eeg
+- medical
+- clinical
+- classification
+- schizophrenia
+- simon task
+---
+# Albrecht2019: Schizophrenia Classification Dataset with Modified Simon Task
+The Albrecht2019 dataset originates from a study investigating reinforcement learning under cognitive conflict in individuals with schizophrenia (PSZ) and healthy controls. The dataset includes both behavioral and EEG recordings collected during a modified Simon task, which introduces response conflict as an implicit cognitive cost during reinforcement learning.
+
+A total of 78 participants took part in the study: 46 individuals with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder, and 32 healthy controls. EEG data were recorded using a 64-channel Brain Vision system at a 1000 Hz sampling rate. Data were preprocessed and artifact-corrected using the EEGLAB pipeline, and ICA was applied for eye-blink removal. EEG epochs were extracted around stimulus and feedback events to capture conflict-evoked and prediction-error-related activity, particularly in the theta band (4–7 Hz).
+
+Participants completed a reinforcement learning version of the Simon task. On each trial, a stimulus was associated with probabilistic reward or punishment outcomes, modulated by whether the trial involved a congruent or conflict-inducing response. This design enabled the dissociation of positive and negative prediction error (PE) learning biases under cognitive effort. A subsequent transfer phase assessed stimulus preferences to infer learning outcomes.
+## Paper
+Albrecht, M. A., Waltz, J. A., Cavanagh, J. F., Frank, M. J., & Gold, J. M. (2019). **Increased conflict-induced slowing, but no differences in conflict-induced positive or negative prediction error learning in patients with schizophrenia**. _Neuropsychologia_, 123, 131-140.
+
+DISCLAIMER: We (DISCO) are NOT the owners or creators of this dataset, but we merely uploaded it here, to support our's ("EEG-Bench") and other's work on EEG benchmarking.
+
+## Dataset Structure
+- `data/` contains the experiment data.
+- `DataDictionary.txt` explains the meaning of columns and codes in various files.
+- `DeID_Dems.csv` gives an overview of the subjects' demographic attributes.
+- `MMN_ElectrodeCoords_MA2.ced` contains information about the EEG electrode positions.
+- `Pre-processing_Pseudo.txt` explains the pre-processing pipeline in pseudocode applied on the EEG data.
+- `scripts/` contains the MATLAB scripts that were used to execute the experiment.
+
+### Filename Format
+
+```
+CC_EEG_s[PID]_[CONDITION].mat
+```
+PID is the patient ID (e.g. `101`), while CONDITION denotes the condition (`P` means "with schizophrenia", `N` means "without schizophrenia"). All patients with PID <= 147 have schizophrenia and all patients with PID >= 149 do NOT have schizophrenia and hence belong to the control group.
+
+### Fields in each File
+A `.mat` file can be read in python as follows:
+```python
+from scipy.io import loadmat
+filename = "CC_EEG_s101_P.mat"
+mat = loadmat(filename, simplify_cells=True)
+```
+(A field "fieldname" can be read from `mat` as `mat["fieldname"]`.)
+
+Then `mat` contains (among others) the following fields and subfields
+- `EEG`
+    - `data`: EEG data of shape `(#channels, time_len)`. E.g. a recording of 30 minutes with 64 channels and a sampling rate of 1000 Hz will have shape `(64, 1800000)`.
+    - `event`: Contains a list of dictionaries, each entry (each event) having the following description:
+        - `latency`: The onset of the event, measured as the index in the time-dimension `time_len`. As noted in the paper, each stimulus has a response window of 850 ms.
+        - `type`: The type of event. This type will be a number of up to 3 digits. In order to decode them, please refer to the `Triggers` section in `DataDictionary.txt`. Typically, a trial starts with a Stimulus Trigger, followed by a Response Trigger (or `105` if no response was given, i.e. the patient did not press a button) and then a Feedback Trigger.
+    - `srate`: Sampling Rate (Hz)
+
+Alternatively, event information for any `CC_EEG_s[PID]_[CONDITION].mat` file can also be read from the accompanying `CC_Beh_s[PID]_[CONDITION].mat` file. There, trials are organized in the 4 blocks described in the paper. Information about a trial can be read as
+```python
+beh_mat = loadmat("CC_Beh_s[PID]_[CONDITION].mat", simplify_cells=True)
+block = 0          # can be 0, 1, 2 or 3
+trial_number = 5   # number of the trial
+trial_info = beh_mat["Beh"][block]["data_block"][trial_number]
+```
+`trial_info` can then be decoded using the `Behavioral Data` section of `DataDictionary.txt`.
+
+## License
+By the original authors of this work, this work has been licensed under the PDDL v1.0 license (see LICENSE.txt).