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Updated ERP code
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‎code/preprocessing-eeg/create_mat.m‎

Lines changed: 146 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -32,7 +32,7 @@
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% dataset_dir = '/Users/fzaki001/thrive-theta-ddm';
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dataset_dir = '/home/data/NDClab/datasets/thrive-dataset/derivatives/preprocessed/';
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% summary_csv_path = '/Users/fzaki001/thrive-theta-ddm/derivatives/behavior/summary.csv';
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summary_csv_path = '/home/data/NDClab/analyses/thrive-theta-ddm/derivatives/behavior/summary-eeg.csv';
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summary_csv_path = '/home/data/NDClab/analyses/thrive-theta-ddm/derivatives/behavior/summary.csv';
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% Setting up other things
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@@ -74,8 +74,6 @@
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end
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%% Count trials
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subjects = [50, 53, 60, 62, 64, 66] % these are just ids to put to the resulting csv, they are not used for computations
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% n_subjects = length(subjects) % this number will be used for loops because the initial subjects list may be changed based on num of trials
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% switch to output directory
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cd(output_location);
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@@ -158,4 +156,148 @@
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%write/append table to disk
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writetable(counts_table, [output_location filesep 'thrive_trialCounts_RespAndStim_', date, '.csv'], "WriteMode", "append");
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end
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end
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%% pull resp-locked erp mat file
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%read in behavioral data for participants
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behavior_info = readtable(summary_csv_path);
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%specify min number of trials per condition (if file contains less than
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%this number for ANY condition, then they will be skipped for ALL conditions
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minTrials = 8;
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%specify min accuracy per condition (if file contains less than
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%this number for ANY condition, then they will be skipped for ALL conditions
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acc_cutoff = .6;
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%initialize participant counter variable (used for indexing into large mat
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%file that data is saved into)
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pIdx = 1;
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%initialize matrices to hold erp data and corresponding sub ids
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erpDat_data = [];
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erpDat_subIds = [];
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% loop through each participant in the study
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for subject = 1:length(datafile_names)
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%initialize numTrials for this participant/file
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numTrials = [];
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% extract participant number
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subNumText = datafile_names{subject}(5:11);
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%find row in behavior file corresponding to this participant
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behavior_id_match_idxs = find(behavior_info{:,'sub'} == str2num(subNumText));
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%if participant has low accuracy in either condition, skip that
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%participant for ALL conditions
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% if (behavior_info{behavior_id_match_idxs,'acc_nonsoc'} < acc_cutoff || behavior_info{behavior_id_match_idxs,'acc_soc'} < acc_cutoff)
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% continue
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% end
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%
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% if (behavior_info{behavior_id_match_idxs,'x6_or_more_err_nonsoc'} < 6 || behavior_info{behavior_id_match_idxs,'x6_or_more_err_soc'} < 6)
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% continue
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% end
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%load the original data set
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EEG = pop_loadset( 'filename', datafile_names{subject}, 'filepath', datafile_paths{subject});
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EEG = eeg_checkset( EEG );
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%remove all the non-stim-locking markers (should have done already...)
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EEG = pop_selectevent( EEG, 'latency','-.1 <= .1','deleteevents','on');
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EEG = eeg_checkset( EEG );
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% NOTE %
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%the logic of checking conditions and then looping over conditions
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%below is fairly hard-coded and could be be substantially improved to
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%allow for easier reuse when number of conditions or number of
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%variables per condition changes.
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%
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%before pulling trials of interest, for any conditions, check to make
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%sure this file/participant has more than minTrials for EACH condition.
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%If the file/participant is below minTrials for even one of the
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%conditions that will be pulled, then the file/participant is skipped
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%entirely and no condition data at all will be pulled for this specific
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%file (but the participant can still have data pulled for another one
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%of their files from another visit).
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%
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%count trials for each condition of interest and store in numTrials vector
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numTrials(1) = length(find( (strcmp({EEG.event.observation}, "s")) & (strcmp({EEG.event.eventType}, "resp")) & (strcmp({EEG.event.congruency}, "i")) & ([EEG.event.accuracy] == 0) & ([EEG.event.responded] == 1) & ([EEG.event.validRt] == 1) ));
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numTrials(2) = length(find( (strcmp({EEG.event.observation}, "s")) & (strcmp({EEG.event.eventType}, "resp")) & (strcmp({EEG.event.congruency}, "i")) & ([EEG.event.accuracy] == 1) & ([EEG.event.responded] == 1) & ([EEG.event.validRt] == 1) ));
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numTrials(3) = length(find( (strcmp({EEG.event.observation}, "ns")) & (strcmp({EEG.event.eventType}, "resp")) & (strcmp({EEG.event.congruency}, "i")) & ([EEG.event.accuracy] == 0) & ([EEG.event.responded] == 1) & ([EEG.event.validRt] == 1) ));
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numTrials(4) = length(find( (strcmp({EEG.event.observation}, "ns")) & (strcmp({EEG.event.eventType}, "resp")) & (strcmp({EEG.event.congruency}, "i")) & ([EEG.event.accuracy] == 1) & ([EEG.event.responded] == 1) & ([EEG.event.validRt] == 1) ));
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%logical test if the number of trials for each condition (numTrials vector)
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%are NOTE all >= minTrials. If statement is true, then participant/file
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%is skipped and for loop over files continues to next file
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% if ~(sum(numTrials >= minTrials) == length(numTrials))
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% continue
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% end
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% loop through conditions of interest for this file (combo of event types)
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%
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% specify number of conditions using a seperate conditionNums var, so
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% that it can be referenced below when iterating idx counters (to only
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%iterate when c == length(conditionNums);
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conditionNums = 1:4;
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%
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for c = conditionNums
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if (c==1) % social error
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observation = 's';
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eventType = 'resp';
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congruency = 'i';
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accuracy = 0;
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responded = 1;
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validRt = 1;
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elseif (c==2) % social correct
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observation = 's';
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eventType = 'resp';
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congruency = 'i';
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accuracy = 1;
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responded = 1;
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validRt = 1;
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elseif (c==3) % nonsocial error
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observation = 'ns';
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eventType = 'resp';
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congruency = 'i';
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accuracy = 0;
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responded = 1;
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validRt = 1;
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elseif (c==4) % nonsocial correct
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observation = 'ns';
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eventType = 'resp';
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congruency = 'i';
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accuracy = 1;
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responded = 1;
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validRt = 1;
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end
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%select combintion of event types of interest based on vars above
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EEG1 = pop_selectevent( EEG, 'latency','-1<=1','observation',observation,'eventType',eventType,'congruency',congruency,'accuracy',accuracy,'responded',responded,'validRt',validRt,'deleteevents','on','deleteepochs','on','invertepochs','off');
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EEG1 = eeg_checkset( EEG1 );
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% Average across epoch dimension
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% this all Channel ERP only needs to be computed once
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% per condition
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meanEpochs = mean(EEG1.data, 3);
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%store data for this condition in array
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erpDat_data(pIdx,c,:,:)= meanEpochs;
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%store participant number for corresponding row in erpdat
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erpDat_subIds{pIdx,1} = datafile_names{subject}(5:11);
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%iterate idx counter IMPORTANT: ONLY ITERATE COUNTER WHEN
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%ON LAST CONDITION
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if c == length(conditionNums)%if this is the last condition of condition loop
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pIdx = pIdx + 1;
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end
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%end loop through conditions
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end
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%end loop through participants
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end
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%save the erps and subject list
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save('thrive_Resp_erps_min_8t_60acc.mat','erpDat_data', 'erpDat_subIds')

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