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Added CSD scripts
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‎code/preprocessing-eeg/create_mat_csd.m‎

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%
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% Modified on 2024/04/11 to process thrive dataset flanker data
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%
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% This script was created by George Buzzell for the NDC Lab EEG Training
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% Workshop on 02/22. This script uses parts of the "set up" structure from
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% the MADE preprocessing pipeline (Debnath, Buzzell, et. al., 2020)
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clear % clear matlab workspace
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clc % clear matlab command window
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%% Setting up other things
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%Location of MADE and ADJUSTED-ADJUST scripts
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% addpath(genpath([main_dir filesep 'MADE-EEG-preprocessing-pipeline']));% enter the path of the EEGLAB folder in this line
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addpath(genpath('/home/data/NDClab/tools/lab-devOps/scripts/MADE_pipeline_standard/eeg_preprocessing'));% enter the path of the folder in this line
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addpath(genpath('/home/data/NDClab/analyses/thrive-theta-ddm/code/matlab/'))
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%Location of "EEG
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% addpath(genpath([main_dir filesep 'eeglab13_4_4b']));% enter the path of the EEGLAB folder in this line
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addpath(genpath('/home/data/NDClab/tools/lab-devOps/scripts/MADE_pipeline_standard/eeglab13_4_4b'));% enter the path of the EEGLAB folder in this line
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%remove path to octave functions inside matlab to prevent errors when
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% rmpath([main_dir filesep 'eeglab13_4_4b' filesep 'functions' filesep 'octavefunc' filesep 'signal'])
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rmpath(['/home/data/NDClab/tools/lab-devOps/scripts/MADE_pipeline_standard/eeglab13_4_4b' filesep 'functions' filesep 'octavefunc' filesep 'signal'])
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%% setup; run this section before any other section below
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%location of analysis folder
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% analysis_dir = '/Users/fzaki001/thrive-theta-ddm';
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analysis_dir = '/home/data/NDClab/analyses/thrive-theta-ddm';
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%location of dataset folder
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% dataset_dir = '/Users/fzaki001/thrive-theta-ddm';
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dataset_dir = '/home/data/NDClab/datasets/thrive-dataset';
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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.csv';
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% Setting up other things
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% 1. Enter the path of the folder that has the data to be analyzed
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data_location = [dataset_dir filesep 'derivatives' filesep 'preprocessed'];
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% 2. Enter the path of the folder where you want to save the postprocessing outputs
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output_location = [analysis_dir filesep 'derivatives' filesep 'preprocessed/erp_check'];
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% 3. this is the correct channel location file BUT INCORRECT PATH!
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% channel_locations = loadbvef('/Users/fzaki001/Downloads/thrive/code/CACS-128-X7-FIXED-64only.bvef');
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channel_locations = loadbvef('/home/data/NDClab/tools/lab-devOps/scripts/MADE_pipeline_standard/eeg_preprocessing/chan_locs_files/electrode_locs_files/CACS-128-X7-FIXED-64only.bvef');
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% %specify parameters of data to process
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%modifying above, to account for files named differently
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%specify parameters of data to process
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task = 'all';
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procStage = 'processed_data';
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visitDirName = 's1_r1'; %visit folder does not list "e1"
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visitFileName = 's1_r1_e1'; %file names include "e1" designation
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% Read files to analyses
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datafile_info=dir([data_location filesep 'sub-*' filesep visitDirName filesep 'eeg' filesep 'sub-*_' task '_eeg_*' procStage '_' visitFileName '.set']);
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datafile_info=datafile_info(~ismember({datafile_info.name},{'.', '..', '.DS_Store'}));
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datafile_names={datafile_info.name};
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datafile_paths={datafile_info.folder};
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[filepath,name,ext] = fileparts(char(datafile_names{1}));
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% Check whether EEGLAB and all necessary plugins are in Matlab path.
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if exist('eeglab','file')==0
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error(['Please make sure EEGLAB is on your Matlab path. Please see EEGLAB' ...
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'wiki page for download and instalation instructions']);
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end
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% Create output folders to save data
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if exist(output_location, 'dir') == 0
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mkdir(output_location)
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end
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for site = 1:64
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trodes{site} = num2str(site)
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end
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Montage_64=ExtractMontage('/home/data/NDClab/analyses/thrive-theta-ddm/code/preprocessing-eeg/64ch_bv_montage_csd.csd', trodes');
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% MapMontage(Montage_64);
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[G, H] = GetGH(Montage_64);
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%% Count trials
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% switch to output directory
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cd(output_location);
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% %create variable names for count trials output and write to disk
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% outputHeader = {'id, s_resp_incon_error, s_resp_incon_corr, ns_resp_incon_error, ns_resp_incon_corr'};
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% dlmwrite(strcat('thrive_trialCounts_respOnly', date, '.csv'), outputHeader, 'delimiter', '', '-append');
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diary(sprintf('erp_log_%s.log', datestr(now, 'mm-dd-yyyy_HH_MM_SS')))
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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 = 6;
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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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for ne = 1:length(EEG.epoch)
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myEEG = single(EEG.data(:, :, ne));
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MyResults = CSD(myEEG, G, H); % compute CSD for <channels-by-samples> 2-D epoch
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data(:, :, ne) = MyResults;
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end
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EEG.data = data
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data(:,:,:) = NaN;
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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_csd_min_6t_60acc.mat','erpDat_data', 'erpDat_subIds')

‎code/preprocessing-eeg/transform_csd.m‎

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%
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% Modified on 2024/04/11 to process thrive dataset flanker data
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%
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% This script was created by George Buzzell for the NDC Lab EEG Training
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% Workshop on 02/22. This script uses parts of the "set up" structure from
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% the MADE preprocessing pipeline (Debnath, Buzzell, et. al., 2020)
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clear % clear matlab workspace
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clc % clear matlab command window
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%% Setting up other things
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%Location of MADE and ADJUSTED-ADJUST scripts
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% addpath(genpath([main_dir filesep 'MADE-EEG-preprocessing-pipeline']));% enter the path of the EEGLAB folder in this line
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addpath(genpath('/home/data/NDClab/tools/lab-devOps/scripts/MADE_pipeline_standard/eeg_preprocessing'));% enter the path of the folder in this line
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addpath(genpath('/home/data/NDClab/analyses/thrive-theta-ddm/code/matlab/'))
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%Location of "EEG
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% addpath(genpath([main_dir filesep 'eeglab13_4_4b']));% enter the path of the EEGLAB folder in this line
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addpath(genpath('/home/data/NDClab/tools/lab-devOps/scripts/MADE_pipeline_standard/eeglab13_4_4b'));% enter the path of the EEGLAB folder in this line
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%remove path to octave functions inside matlab to prevent errors when
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% rmpath([main_dir filesep 'eeglab13_4_4b' filesep 'functions' filesep 'octavefunc' filesep 'signal'])
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rmpath(['/home/data/NDClab/tools/lab-devOps/scripts/MADE_pipeline_standard/eeglab13_4_4b' filesep 'functions' filesep 'octavefunc' filesep 'signal'])
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%% setup; run this section before any other section below
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%location of analysis folder
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analysis_dir = '/home/data/NDClab/analyses/thrive-theta-ddm';
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%location of dataset folder
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dataset_dir = '/home/data/NDClab/datasets/thrive-dataset';
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% Setting up other things
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% 1. Enter the path of the folder that has the data to be analyzed
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data_location = [dataset_dir filesep 'derivatives' filesep 'preprocessed'];
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% 2. Enter the path of the folder where you want to save the postprocessing outputs
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output_location = [analysis_dir filesep 'derivatives' filesep 'preprocessed/erp_check'];
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save_location = [analysis_dir filesep 'derivatives' filesep 'preprocessed/csd_data'];
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% 3. this is the correct channel location file BUT INCORRECT PATH!
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%modifying above, to account for files named differently
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%specify parameters of data to process
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task = 'all';
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procStage = 'processed_data';
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visitDirName = 's1_r1'; %visit folder does not list "e1"
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visitFileName = 's1_r1_e1'; %file names include "e1" designation
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% Read files to analyses
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datafile_info=dir([data_location filesep 'sub-*' filesep visitDirName filesep 'eeg' filesep 'sub-*_' task '_eeg_*' procStage '_' visitFileName '.set']);
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datafile_info=datafile_info(~ismember({datafile_info.name},{'.', '..', '.DS_Store'}));
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datafile_names={datafile_info.name};
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datafile_paths={datafile_info.folder};
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[filepath,name,ext] = fileparts(char(datafile_names{1}));
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% Check whether EEGLAB and all necessary plugins are in Matlab path.
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if exist('eeglab','file')==0
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error(['Please make sure EEGLAB is on your Matlab path. Please see EEGLAB' ...
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'wiki page for download and instalation instructions']);
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end
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% Create output folders to save data
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if exist(save_location, 'dir') == 0
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mkdir(save_location);
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end
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for site = 1:64;
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trodes{site} = num2str(site);
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end
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Montage_64=ExtractMontage('/home/data/NDClab/analyses/thrive-theta-ddm/code/preprocessing-eeg/64ch_bv_montage_csd.csd', trodes');
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% MapMontage(Montage_64);
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[G, H] = GetGH(Montage_64);
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diary(sprintf('csd_log_%s.log', datestr(now, 'mm-dd-yyyy_HH_MM_SS')))
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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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% extract participant number
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subNumText = datafile_names{subject}(5:11);
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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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disp(datafile_names{subject})
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disp(save_location)
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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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for ne = 1:length(EEG.epoch)
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myEEG = single(EEG.data(:, :, ne));
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MyResults = CSD(myEEG, G, H); % compute CSD for <channels-by-samples> 2-D epoch
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data(:, :, ne) = MyResults;
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end
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EEG.data = data;
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data(:,:,:) = NaN;
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EEG = pop_editset(EEG, 'setname', datafile_names{subject});
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EEG = pop_saveset(EEG, 'filename', datafile_names{subject}, 'filepath', save_location);
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end

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