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run_MADE.m
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function run_MADE(output_dir_name, bids_dir, participant_label, ...
session_label, file_extension, ...
json_settings_file, save_interim_result)
% ************************************************************************
% The Maryland Analysis of Developmental EEG (UMADE) Pipeline
% Version 1.0
% Developed at the Child Development Lab, University of Maryland, College Park
% Original Contributors to MADE pipeline:
% Ranjan Debnath (rdebnath@umd.edu)
% George A. Buzzell (gbuzzell@umd.edu)
% Santiago Morales Pamplona (moraless@umd.edu)
% Stephanie Leach (sleach12@umd.edu)
% Maureen Elizabeth Bowers (mbowers1@umd.edu)
% Nathan A. Fox (fox@umd.edu)
% Ongoing Contributors:
% Lydia Yoder (lyoder@umd.edu)
% Erik Lee (leex6144@umn.edu)
% Martin Antunez Garcia (mantunez@umd.edu )
% Marco McSweeney (mmcsw1@umd.edu)
% MADE uses EEGLAB toolbox and some of its plugins. Before running the pipeline, you have to install the following:
% EEGLab: https://sccn.ucsd.edu/eeglab/downloadtoolbox.php/download.php
% You also need to download the following plugins/extensions from here: https://sccn.ucsd.edu/wiki/EEGLAB_Extensions
% Specifically, download:
% MFFMatlabIO: https://github.com/arnodelorme/mffmatlabio/blob/master/README.txt
% FASTER: https://sourceforge.net/projects/faster/
% ADJUST: https://www.nitrc.org/projects/adjust/
% Adjusted ADJUST (included in this pipeline): https://github.com/ChildDevLab/MADE-EEG-preprocessing-pipeline
% After downloading these plugins (as zip files), you need to place it in the eeglab/plugins folder.
% For instance, for FASTER, you uncompress the downloaded extension file (e.g., 'FASTER.zip') and place it in the main EEGLAB "plugins" sub-directory/sub-folder.
% After placing all the required plugins, add the EEGLAB folder to your path by using the following code:
% addpath(genpath(('...')) % Enter the path of the EEGLAB folder in this line
% Please cite the following references for in any manuscripts produced utilizing MADE pipeline:
% EEGLAB: A Delorme & S Makeig (2004) EEGLAB: an open source toolbox for
% analysis of single-trial EEG dynamics. Journal of Neuroscience Methods, 134, 9?21.
% firfilt (filter plugin): developed by Andreas Widmann (https://home.uni-leipzig.de/biocog/content/de/mitarbeiter/widmann/eeglab-plugins/)
% FASTER: Nolan, H., Whelan, R., Reilly, R.B., 2010. FASTER: Fully Automated Statistical
% Thresholding for EEG artifact Rejection. Journal of Neuroscience Methods, 192, 152?162.
% ADJUST: Mognon, A., Jovicich, J., Bruzzone, L., Buiatti, M., 2011. ADJUST: An automatic EEG
% artifact detector based on the joint use of spatial and temporal features. Psychophysiology, 48, 229?240.
% Our group has modified ADJUST plugin to improve selection of ICA components containing artifacts
% This pipeline is released under the GNU General Public License version 3.
% ************************************************************************
%% User input: user provide relevant information to be used for data processing
% Preprocessing of EEG data involves using some common parameters for
% every subject. This part of the script initializes the common parameters.
%clear % clear matlab workspace
%clc % clear matlab command window
%addpath(genpath('/home/faird/shared/data/TOTS_UMD_collab/code/cdl-eeg-processing/MADE-EEG-preprocessing-pipeline'))
%addpath(genpath('/home/faird/shared/data/TOTS_UMD_collab/code/cdl-eeg-processing/MADE-EEG-preprocessing-pipeline/eeglab2021.0'));% enter the path of the EEGLAB folder in this line
%% Read files to analyses
%datafile_names=dir(rawdata_location);
force_session_definition = 1; %Since ICA denoising happens at the session
%level, be sure either the session is defined
%or the participant has one session.
datafile_names = find_files_to_proc(bids_dir, participant_label, session_label, ...
'', file_extension, force_session_definition); %Commenting out task label because this should always be run at the session level
%datafile_names=dir([rawdata_location '/*.set']);
datafile_dirs={datafile_names.folder};
datafile_names={datafile_names.name};
[filepath,name,ext] = fileparts(char(datafile_names{1}));
%% Check whether EEGLAB and all necessary plugins are in Matlab path.
check_if_plugins_are_present(ext);
%% Loop over all data files
for run=1:length(datafile_names)
rawdata_location = datafile_dirs{run}; %update the input path for the given run
fprintf('\n\n\n*** Processing run %d (%s) ***\n\n\n', run, datafile_names{run});
sub_id(run) = string(participant_label);
pat = 'run-' + digitsPattern(2);
run_label = extract(datafile_names{run}, pat);
if isempty(run_label)
run_label = 'run-01';
end
task_label = strcat('task', extractBetween(datafile_names{run}, '_task', '_'));
%% STEP 1: Import EGI data file and relevant information
EEG = pop_loadset('filename',datafile_names{run},'filepath',rawdata_location);
temp_split = split(datafile_names{run}, '_task');
subses = temp_split{end - 1}; %Its okay that this will get updated each iteration
%since it will be the same each time.
% Edit this data import function and use appropriate plugin from EEGLAB
% for non-.mff data. For example, to import biosemi data, use biosig plugin.
% The example codes for 64 channels biosemi data:
% EEG = pop_biosig([rawdata_location, filesep, datafile_names{run}]);
% EEG = eeg_checkset(EEG);
% EEG = pop_select( EEG,'nochannel', 65:72); % delete redundant channels
%% TM - 8/6/2024: Impedances catch
% Check if impedances were turned on and off -- if so pop out the
% section with impedances and save
% will not catch if impdances was turned on before task was
% started or aff after task was finished
if numel(find(strcmp({EEG.event.type}, 'IBEG')))>0 && numel(find(strcmp({EEG.event.type}, 'IEND')))>0
startidx = find(strcmp({EEG.event.type}, 'IBEG'));
endidx = find(strcmp({EEG.event.type}, 'IEND'));
if length(startidx) == 1 && length(endidx) == 1
impstart = EEG.event(startidx).onset - 0.1;
impend = EEG.event(endidx).onset;
else
error('multiple Impedance flags, check raw data and fix manually please');
end
EEG = pop_select(EEG, 'rmtime', [impstart impend]);
end
%% TM - 8/6/2024 Catch DRPS flag and throw error
if numel(find(strcmp({EEG.event.type}, 'DrpS')))>0
error('DrpS flag found, check raw data and fix manually please');
end
%% Step 1.25: Load settings for processing
% 2. Enter the path of the folder where you want to save the processed data
s = grab_settings(datafile_names{run}, json_settings_file);
%NEW - to cover case where boundary marker changes
boundary_marker = s.boundary_marker;
%NEW - to cover case where EKG channel is present
if isfield(s, 'ekg_channels')
ekg_channels = s.ekg_channels;
else
ekg_channels = {};
end
% 3. Enter the path of the channel location file
%channel_locations = ['path to eeglab folder' filesep 'sample_locs' filesep 'GSN128.sfp'];
channel_locations = s.channel_locations;
% 5. Do you want to down sample the data?
down_sample = s.down_sample; % 0 = NO (no down sampling), 1 = YES (down sampling)
sampling_rate = s.sampling_rate; % set sampling rate (in Hz), if you want to down sample
% 6. Do you want to delete the outer layer of the channels? (Rationale has been described in MADE manuscript)
% This fnction can also be used to down sample electrodes. For example, if EEG was recorded with 128 channels but you would
% like to analyse only 64 channels, you can assign the list of channnels to be excluded in the 'outerlayer_channel' variable.
delete_outerlayer = s.delete_outerlayer; % 0 = NO (do not delete outer layer), 1 = YES (delete outerlayer);
% If you want to delete outer layer, make a list of channels to be deleted
outerlayer_channel = s.outerlayer_channel; % list of channels
% recommended list for EGI 128 chanenl net: {'E17' 'E38' 'E43' 'E44' 'E48' 'E49' 'E113' 'E114' 'E119' 'E120' 'E121' 'E125' 'E126' 'E127' 'E128' 'E56' 'E63' 'E68' 'E73' 'E81' 'E88' 'E94' 'E99' 'E107'}
% 7. Initialize the filters
highpass = s.highpass; % High-pass frequency
lowpass = s.lowpass; % Low-pass frequency. We recommend low-pass filter at/below line noise frequency (see manuscript for detail)
% 14. Do you want to rereference your data?
rerefer_data = s.rerefer_data; % 0 = NO, 1 = YES
reref=s.reref; % Enter electrode name/s or number/s to be used for rereferencing
% For channel name/s enter, reref = {'channel_name', 'channel_name'};
% For channel number/s enter, reref = [channel_number, channel_number];
% For average rereference enter, reref = []; default is average rereference
% 16. How do you want to save your data? .set or .mat
output_format = s.output_format; % 1 = .set (EEGLAB data structure), 2 = .mat (Matlab data structure)
%% Step 1.3 Create output folders to save data
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%Format the output path to be the same as the input path at %%%%%%%
%%%%%the level of the run folder onwards %%%%%%%%%%%%%%%%%%%%%%%%%%
partial_path_index = strfind(rawdata_location, '/sub');
if length(partial_path_index) > 1
partial_path_index = partial_path_index(end);
end
output_location = fullfile(output_dir_name, rawdata_location(partial_path_index:end));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
if ischar(save_interim_result)
save_interim_result = str2double(save_interim_result);
end
if exist(output_location, 'dir') == 0
mkdir(output_location);
end
if exist([output_location filesep 'filtered_data'], 'dir') == 0
mkdir([output_location filesep 'filtered_data'])
end
if exist([output_location filesep 'ica_data'], 'dir') == 0
mkdir([output_location filesep 'ica_data'])
end
if exist([output_location filesep 'processed_data'], 'dir') == 0
mkdir([output_location filesep 'processed_data'])
end
if exist([output_location filesep 'merged_data'], 'dir') == 0
mkdir([output_location filesep 'merged_data'])
end
cd(output_location); %Go to output dir
%% Step 1.4: Save specification
save_specification(s, output_location, datafile_names{run});
%% STEP 1.5: Delete discontinuous data from the raw data file (OPTIONAL, but necessary for most EGI files)
% Note: code below may need to be modified to select the appropriate markers (depends on the import function)
% remove discontinous data at the start of the file
disconMarkers = find(strcmp({EEG.event.type}, boundary_marker)); % boundary markers often indicate discontinuity
if isempty(disconMarkers) == false
EEG = eeg_eegrej( EEG, [1 EEG.event(disconMarkers(1)).latency] ); % remove discontinuous chunk... if not EGI, MODIFY BEFORE USING THIS SECTION
EEG = eeg_checkset( EEG );
end
%% STEP 2: Import channel locations
EEG=pop_chanedit(EEG, 'load',{channel_locations 'filetype' 'autodetect'});
EEG = eeg_checkset( EEG );
% Check whether the channel locations were properly imported. The EEG signals and channel numbers should be same.
if size(EEG.data, 1) ~= length(EEG.chanlocs)
error('The size of the data does not match with channel numbers.');
end
%% STEP 2.5: Delete EKG Channels if they are present
%This chunk of code should be good but needs to be tested. Also, need
%to be sure the check for the number of electrodes in step 2 isn't
%thrown off by having the ekg channels.
if size(ekg_channels, 2) > 0
nbchans=cell(1,EEG.nbchan);
for i=1:EEG.nbchan
nbchans{i}= EEG.chanlocs(i).labels;
end
[~,chansidx] = ismember(ekg_channels, nbchans);
RemChans_Idx = chansidx(chansidx ~= 0);
EEG = eeg_checkset( EEG );
EEG = pop_select( EEG,'nochannel', RemChans_Idx);
end
%% STEP 3: Adjust anti-aliasing and task related time offset
%if adjust_time_offset==1
% adjust anti-aliasing filter time offset
%if filter_timeoffset~=0
% for aafto=1:length(EEG.event)
% EEG.event(aafto).latency=EEG.event(aafto).latency+(filter_timeoffset/1000)*EEG.srate;
% end
%end
% adjust stimulus time offset
%if stimulus_timeoffset~=0
% for sto=1:length(EEG.event)
% for sm=1:length(stimulus_markers)
% if strcmp(EEG.event(sto).type, stimulus_markers{sm})
% EEG.event(sto).latency=EEG.event(sto).latency+(stimulus_timeoffset/1000)*EEG.srate;
% end
% end
% end
%end
% adjust response time offset
%if response_timeoffset~=0
% for rto=1:length(EEG.event)
% for rm=1:length(response_markers)
% if strcmp(EEG.event(rto).type, response_markers{rm})
% EEG.event(rto).latency=EEG.event(rto).latency-(response_timeoffset/1000)*EEG.srate;
% end
% end
% end
%end
%end
%% STEP 4: Change sampling rate
if down_sample==1
if floor(sampling_rate) > EEG.srate
error ('Sampling rate cannot be higher than recorded sampling rate');
elseif floor(sampling_rate) ~= EEG.srate
EEG = pop_resample( EEG, sampling_rate);
EEG = eeg_checkset( EEG );
end
end
%% STEP 5: Delete outer layer of channels
chans_labels=cell(1,EEG.nbchan);
for i=1:EEG.nbchan
chans_labels{i}= EEG.chanlocs(i).labels;
end
[chans,chansidx] = ismember(outerlayer_channel, chans_labels);
outerlayer_channel_idx = chansidx(chansidx ~= 0);
if delete_outerlayer==1
if isempty(outerlayer_channel_idx)==1
error(['None of the outer layer channels present in channel locations of data.'...
' Make sure outer layer channels are present in channel labels of data (EEG.chanlocs.labels).']);
else
EEG = pop_select( EEG,'nochannel', outerlayer_channel_idx);
EEG = eeg_checkset( EEG );
end
end
%% STEP 5.25: Label Task Variable and DIN conidtions if it is not already labeled
if strcmp(EEG.event(3).Task, 'n/a')
if contains(EEG.filename, 'MMN')
task = 'MMN';
din2s = find(strcmp({EEG.event.type}, 'DIN2'));
for d =1:length(din2s) %label the DIN condition
EEG.event(din2s(d)).Condition = EEG.event(din2s(d)-1).Condition;
end
num_stm = numel(find(strcmp({EEG.event.type}, 'stms')));
num_din = length(din2s);
if num_stm < num_din %remove any extra DINs
for w = 1:length(din2s)
if ~(strcmp({EEG.event(din2s(w)-1).type}, 'stms'))
EEG.event(din2s(w)).type = 'EXTRA_DIN';
end
end
end
elseif contains(EEG.filename, 'RS')
task = 'RS'; %no labeling needed
elseif contains(EEG.filename, 'VEP')
task = 'VEP';
din3s = find(strcmp({EEG.event.type}, 'DIN3'));
if din3s(1) == 1 %if the first flag in a file is a DIN remove it
EEG.event(din3s(1)).type = 'EXTRA_DIN';
end
for d =1:length(din3s) %label the DIN conditions
EEG.event(din3s(d)).Condition = EEG.event(din3s(d)-1).Condition;
end
elseif contains(EEG.filename, 'FACE')
task = 'FACE';
dins = find(strcmp({EEG.event.type}, 'DIN3'));
%label Face Blocks
if length(dins) >= 100
block1 = EEG.event(1:dins(100));
else
block1 = EEG.event;
end
searchblock1_inverted = numel(find(strcmp({block1.Condition}, '2')))-1; %subtract 1 bc there is always 1 flag of each condition in the SESS rows
if searchblock1_inverted >=1
upright_condition_b1 = '1';
upright_condition_b2 = '4';
else
upright_condition_b1 = '4';
upright_condition_b2 = '1';
end
for d =1:length(dins)
%The condition for the din is set equal to whatever the condition of preceding flag
EEG.event(dins(d)).Condition = EEG.event(dins(d)-1).Condition;
if d <=100
EEG.event(dins(d)).Block = 1;
if strcmp(EEG.event(dins(d)-1).Condition, '1')
EEG.event(dins(d)).Condition = upright_condition_b1;
EEG.event(dins(d)-1).Condition = upright_condition_b1;
EEG.event(dins(d)+1).Condition = upright_condition_b1;
end
else
EEG.event(dins(d)).Block = 2;
if strcmp(EEG.event(dins(d)-1).Condition, '1')
EEG.event(dins(d)).Condition = upright_condition_b2;
EEG.event(dins(d)-1).Condition = upright_condition_b2;
EEG.event(dins(d)-2).Condition = upright_condition_b2;
end
end
end
num_stm = numel(find(strcmp({EEG.event.type}, 'stm+')));
num_din = length(dins);
if num_stm < num_din
for w = 1:length(dins)
if ~strcmp({EEG.event(dins(w)-1).type}, 'stm+')
EEG.event(dins(w)).type = 'EXTRA_DIN';
end
end
end
end
for i = 1:length(EEG.event)
EEG.event(i).Task = task; %label task variable
end
end
%% STEP 5.5: Get Line Noise Measure
% from HAPPE pipeline: see https://github.com/PINE-Lab/HAPPE for details
lineNoiseIn = struct('lineNoiseMethod', 'clean', ...
'lineNoiseChannels', 1:EEG.nbchan, 'Fs', EEG.srate, ...
'lineFrequencies', [60 120], 'p', 0.01, 'fScanBandWidth', 2, ...
'taperBandWidth', 2, 'taperWindowSize', 4, 'taperWindowStep', 4, ...
'tau', 100, 'pad', 2, 'fPassBand', [0 EEG.srate/2], ...
'maximumIterations', 10);
[outEEG, ~] = cleanLineNoise(EEG, lineNoiseIn);
neighbors = [59,60,61];
lnParams_harms_frequs = [];
lnMeans = [];
% LINE NOISE REDUCTION QM: Assesses the performance of line noise reduction.
lnMeans = assessPipelineStep('line noise reduction', reshape(EEG.data, ...
size(EEG.data, 1), []), reshape(outEEG.data, size(outEEG.data,1), ...
[]), lnMeans, EEG.srate, [neighbors lnParams_harms_frequs]) ;
lineNoise{run,1} = lnMeans(3); %grab only the 60 hz pre/post r value
%% STEP 6: Filter data
% Calculate filter order using the formula: m = dF / (df / fs), where m = filter order,
% df = transition band width, dF = normalized transition width, fs = sampling rate
% dF is specific for the window type. Hamming window dF = 3.3
high_transband = highpass; % high pass transition band
low_transband = 10; % low pass transition band
hp_fl_order = 3.3 / (high_transband / EEG.srate);
lp_fl_order = 3.3 / (low_transband / EEG.srate);
% Round filter order to next higher even integer. Filter order is always even integer.
if mod(floor(hp_fl_order),2) == 0
hp_fl_order=floor(hp_fl_order);
elseif mod(floor(hp_fl_order),2) == 1
hp_fl_order=floor(hp_fl_order)+1;
end
if mod(floor(lp_fl_order),2) == 0
lp_fl_order=floor(lp_fl_order)+2;
elseif mod(floor(lp_fl_order),2) == 1
lp_fl_order=floor(lp_fl_order)+1;
end
% Calculate cutoff frequency
high_cutoff = highpass/2;
low_cutoff = lowpass + (low_transband/2);
% Performing high pass filtering
EEG = eeg_checkset( EEG );
EEG = pop_firws(EEG, 'fcutoff', high_cutoff, 'ftype', 'highpass', 'wtype', 'hamming', 'forder', hp_fl_order, 'minphase', 0);
EEG = eeg_checkset( EEG );
% % % % % % % % % % % % % % % % % % % % % % % % % % % % % % %
% pop_firws() - filter window type hamming ('wtype', 'hamming')
% pop_firws() - applying zero-phase (non-causal) filter ('minphase', 0)
% Performing low pass filtering
EEG = eeg_checkset( EEG );
EEG = pop_firws(EEG, 'fcutoff', low_cutoff, 'ftype', 'lowpass', 'wtype', 'hamming', 'forder', lp_fl_order, 'minphase', 0);
EEG = eeg_checkset( EEG );
% pop_firws() - transition band width: 10 Hz
% pop_firws() - filter window type hamming ('wtype', 'hamming')
% pop_firws() - applying zero-phase (non-causal) filter ('minphase', 0)
%% Step 6.5: Save individual files
if output_format==1
EEG = eeg_checkset( EEG );
EEG = pop_editset(EEG, 'setname', strrep(datafile_names{run}, '_eeg.set', '_desc-filtered_eeg'));
EEG = pop_saveset( EEG,'filename',strrep(datafile_names{run}, '_eeg.set', '_desc-filtered_eeg.set'),'filepath', [output_location filesep 'filtered_data' filesep]); % save .set format
elseif output_format==2
save([[output_location filesep 'filtered_data' filesep ] strrep(datafile_names{run}, '_eeg.set', '_desc-filtered_eeg.mat')], 'EEG'); % save .mat format
end
end
%% Step 6.7: Merge Data (based off of shared script from lydia)
[EEG, event_struct] = merge_data(output_location, file_extension, subses);
%% Initialize output variables
reference_used_for_faster=[]; % reference channel used for running faster to identify bad channel/s
faster_bad_channels=[]; % number of bad channel/s identified by faster
ica_preparation_bad_channels=[]; % number of bad channel/s due to channel/s exceeding xx% of artifacted epochs
length_ica_data=[]; % length of data (in second) fed into ICA decomposition
total_ICs=[]; % total independent components (ICs)
ICs_removed=[]; % number of artifacted ICs
total_epochs_before_artifact_rejection=[];
total_epochs_after_artifact_rejection=[];
total_channels_interpolated=[]; % total_channels_interpolated=faster_bad_channels+ica_preparation_bad_channels
%% STEP 7: Run faster to find bad channels
% First check whether reference channel (i.e. zeroed channels) is present in data
% reference channel is needed to run faster
ref_chan=[]; FASTbadChans=[]; all_chan_bad_FAST=0;
ref_chan=find(any(EEG.data, 2)==0);
if numel(ref_chan)>1
error(['There are more than 1 zeroed channel (i.e. zero value throughout recording) in data.'...
' Only reference channel should be zeroed channel. Delete the zeroed channel/s which is not reference channel.']);
elseif numel(ref_chan)==1
list_properties = channel_properties(EEG, 1:EEG.nbchan, ref_chan); % run faster
FASTbadIdx=min_z(list_properties);
FASTbadChans=find(FASTbadIdx==1);
FASTbadChans=FASTbadChans(FASTbadChans~=ref_chan);
for i = 1 : run %since faster is the same for all runs
reference_used_for_faster{i}={EEG.chanlocs(ref_chan).labels};
end
EEG = eeg_checkset(EEG);
channels_analysed=EEG.chanlocs; % keep full channel locations to use later for interpolation of bad channels
elseif numel(ref_chan)==0
warning('Reference channel is not present in data. Cz channel will be used as reference channel.');
ref_chan=find(strcmp({EEG.chanlocs.labels}, 'Cz')); % find Cz channel index
EEG_copy=[];
EEG_copy=EEG; % make a copy of the dataset
EEG_copy = pop_reref( EEG_copy, ref_chan,'keepref','on'); % rerefer to Cz in copied dataset
EEG_copy = eeg_checkset(EEG_copy);
list_properties = channel_properties(EEG_copy, 1:EEG_copy.nbchan, ref_chan); % run faster on copied dataset
FASTbadIdx=min_z(list_properties);
FASTbadChans=find(FASTbadIdx==1);
channels_analysed=EEG.chanlocs;
for i = 1 : run %since faster is the same for all runs
reference_used_for_faster{i}={EEG.chanlocs(ref_chan).labels};
end
end
if numel(FASTbadChans)==0 %LY moved this if statement to occur prior to channel rejection 6/5/2024
for i = 1 : run
faster_bad_channels{i}='0';
end
else
for i = 1 : run
%LY change from using indexes to channel names 4/15/2024
bad_chan_labels = strjoin({EEG.chanlocs(FASTbadChans).labels}, ' ');
%bad_chan_labels = strrep(bad_chan_labels, 'E', '');
%faster_bad_channels{i}=num2str(FASTbadChans');
faster_bad_channels{i}= bad_chan_labels;
end
end
% If FASTER identifies all channels as bad channels, save the dataset
% at this stage and ignore the remaining of the preprocessing.
if numel(FASTbadChans)==EEG.nbchan || numel(FASTbadChans)+1==EEG.nbchan
all_chan_bad_FAST=1;
warning(['No usable data for datafile', datafile_names{run}]);
if output_format==1
EEG = eeg_checkset(EEG);
EEG = pop_editset(EEG, 'setname', strrep(datafile_names{run}, ext, '_no_usable_data_all_bad_channels'));
EEG = pop_saveset(EEG, 'filename', strrep(datafile_names{run}, ext, '_no_usable_data_all_bad_channels.set'),'filepath', [output_location filesep 'processed_data' filesep ]); % save .set format
elseif output_format==2
save([[output_location filesep 'processed_data' filesep ] strrep(datafile_names{run}, ext, '_no_usable_data_all_bad_channels.mat')], 'EEG'); % save .mat format
end
else
% Reject channels that are bad as identified by Faster
EEG = pop_select( EEG,'nochannel', FASTbadChans);
EEG = eeg_checkset(EEG);
if numel(ref_chan)==1
ref_chan=find(any(EEG.data, 2)==0);
EEG = pop_select( EEG,'nochannel', ref_chan); % remove reference channel
end
end
% if numel(FASTbadChans)==0 %TEMP
% for i = 1 : run
% faster_bad_channels{i}='0';
% end
% else
% for i = 1 : run
% %LY change from using indexes to channel names 4/15/2024
% bad_chan_labels = strjoin({EEG.chanlocs(FASTbadChans).labels}, ' ');
% %bad_chan_labels = strrep(bad_chan_labels, 'E', '');
% %faster_bad_channels{i}=num2str(FASTbadChans');
% faster_bad_channels{i}= bad_chan_labels;
% end
% end
if all_chan_bad_FAST==1
for i = 1 : run
faster_bad_channels{i}='0';
ica_preparation_bad_channels{i}='0';
length_ica_data(i)=0;
total_ICs(i)=0;
ICs_removed{i}='0';
total_epochs_before_artifact_rejection(i)=0;
total_epochs_after_artifact_rejection(i)=0;
total_channels_interpolated(i)=0;
end
end
%% STEP 8: Prepare data for ICA
EEG_copy=[];
EEG_copy=EEG; % make a copy of the dataset
EEG_copy = eeg_checkset(EEG_copy);
% Perform 1Hz high pass filter on copied dataset
transband = 1;
fl_cutoff = transband/2;
fl_order = 3.3 / (transband / EEG.srate);
if mod(floor(fl_order),2) == 0
fl_order=floor(fl_order);
elseif mod(floor(fl_order),2) == 1
fl_order=floor(fl_order)+1;
end
EEG_copy = pop_firws(EEG_copy, 'fcutoff', fl_cutoff, 'ftype', 'highpass', 'wtype', 'hamming', 'forder', fl_order, 'minphase', 0);
EEG_copy = eeg_checkset(EEG_copy);
% Create 1 second epoch
EEG_copy=eeg_regepochs(EEG_copy,'recurrence', 1, 'limits',[0 1], 'rmbase', [NaN], 'eventtype', '999'); % insert temporary marker 1 second apart and create epochs
EEG_copy = eeg_checkset(EEG_copy);
% Find bad epochs and delete them from dataset
vol_thrs = [-1000 1000]; % [lower upper] threshold limit(s) in uV.
emg_thrs = [-100 30]; % [lower upper] threshold limit(s) in dB.
emg_freqs_limit = [20 40]; % [lower upper] frequency limit(s) in Hz.
% Find channel/s with xx% of artifacted 1-second epochs and delete them
chanCounter = 1; ica_prep_badChans = [];
numEpochs =EEG_copy.trials; % find the number of epochs
all_bad_channels=0;
for ch=1:EEG_copy.nbchan
% Find artifaceted epochs by detecting outlier voltage
EEG_copy = pop_eegthresh(EEG_copy,1, ch, vol_thrs(1), vol_thrs(2), EEG_copy.xmin, EEG_copy.xmax, 0, 0);
EEG_copy = eeg_checkset( EEG_copy );
% 1 : data type (1: electrode, 0: component)
% 0 : display with previously marked rejections? (0: no, 1: yes)
% 0 : reject marked trials? (0: no (but store the marks), 1:yes)
% Find artifaceted epochs by using thresholding of frequencies in the data.
% this method mainly rejects muscle movement (EMG) artifacts
EEG_copy = pop_rejspec( EEG_copy, 1,'elecrange',ch ,'method','fft','threshold', emg_thrs, 'freqlimits', emg_freqs_limit, 'eegplotplotallrej', 0, 'eegplotreject', 0);
% method : method to compute spectrum (fft)
% threshold : [lower upper] threshold limit(s) in dB.
% freqlimits : [lower upper] frequency limit(s) in Hz.
% eegplotplotallrej : 0 = Do not superpose rejection marks on previous marks stored in the dataset.
% eegplotreject : 0 = Do not reject marked trials (but store the marks).
% Find number of artifacted epochs
EEG_copy = eeg_checkset( EEG_copy );
EEG_copy = eeg_rejsuperpose( EEG_copy, 1, 1, 1, 1, 1, 1, 1, 1);
artifacted_epochs=EEG_copy.reject.rejglobal;
% Find bad channel / channel with more than 20% artifacted epochs
if sum(artifacted_epochs) > (numEpochs*20/100)
ica_prep_badChans(chanCounter) = ch;
chanCounter=chanCounter+1;
end
end
% If all channels are bad, save the dataset at this stage and ignore the remaining of the preprocessing.
if numel(ica_prep_badChans)==EEG.nbchan || numel(ica_prep_badChans)+1==EEG.nbchan
all_bad_channels=1;
warning(['No usable data for datafile', datafile_names{run}]);
if output_format==1
EEG = eeg_checkset(EEG);
EEG = pop_editset(EEG, 'setname', strrep(datafile_names{run}, ext, '_no_usable_data_all_bad_channels'));
EEG = pop_saveset(EEG, 'filename', strrep(datafile_names{run}, ext, '_no_usable_data_all_bad_channels.set'),'filepath', [output_location filesep 'processed_data' filesep ]); % save .set format
elseif output_format==2
save([[output_location filesep 'processed_data' filesep ] strrep(datafile_names{run}, ext, '_no_usable_data_all_bad_channels.mat')], 'EEG'); % save .mat format
end
else
% Reject bad channel - channel with more than xx% artifacted epochs
EEG_copy = pop_select( EEG_copy,'nochannel', ica_prep_badChans);
EEG_copy = eeg_checkset(EEG_copy);
end
for i = 1 : run
if numel(ica_prep_badChans)==0
ica_preparation_bad_channels{i}='0';
else
ica_preparation_bad_channels{i}=num2str(ica_prep_badChans);
end
end
if all_bad_channels == 1
for i = 1 : run
length_ica_data(i)=0;
total_ICs(i)=0;
ICs_removed{i}='0';
total_epochs_before_artifact_rejection(i)=0;
total_epochs_after_artifact_rejection(i)=0;
total_channels_interpolated(i)=0;
end
end
% Find the artifacted epochs across all channels and reject them before doing ICA.
EEG_copy = pop_eegthresh(EEG_copy,1, 1:EEG_copy.nbchan, vol_thrs(1), vol_thrs(2), EEG_copy.xmin, EEG_copy.xmax,0,0);
EEG_copy = eeg_checkset(EEG_copy);
% 1 : data type (1: electrode, 0: component)
% 0 : display with previously marked rejections? (0: no, 1: yes)
% 0 : reject marked trials? (0: no (but store the marks), 1:yes)
% Find artifaceted epochs by using power threshold in 20-40Hz frequency band.
% This method mainly rejects muscle movement (EMG) artifacts.
EEG_copy = pop_rejspec(EEG_copy, 1,'elecrange', 1:EEG_copy.nbchan, 'method', 'fft', 'threshold', emg_thrs ,'freqlimits', emg_freqs_limit, 'eegplotplotallrej', 0, 'eegplotreject', 0);
% method : method to compute spectrum (fft)
% threshold : [lower upper] threshold limit(s) in dB.
% freqlimits : [lower upper] frequency limit(s) in Hz.
% eegplotplotallrej : 0 = Do not superpose rejection marks on previous marks stored in the dataset.
% eegplotreject : 0 = Do not reject marked trials (but store the marks).
% Find the number of artifacted epochs and reject them
EEG_copy = eeg_checkset(EEG_copy);
EEG_copy = eeg_rejsuperpose(EEG_copy, 1, 1, 1, 1, 1, 1, 1, 1);
reject_artifacted_epochs=EEG_copy.reject.rejglobal;
EEG_copy = pop_rejepoch(EEG_copy, reject_artifacted_epochs, 0);
%% STEP 9: Run ICA
for i = 1 : run
length_ica_data(i)=EEG_copy.trials; % length of data (in second) fed into ICA
end
EEG_copy = eeg_checkset(EEG_copy);
%LY skip ICA for testing
EEG_copy = pop_runica(EEG_copy, 'icatype', 'runica', 'extended', 1, 'stop', 1E-7, 'interupt','off');
% Find the ICA weights that would be transferred to the original dataset
ICA_WINV=EEG_copy.icawinv;
ICA_SPHERE=EEG_copy.icasphere;
ICA_WEIGHTS=EEG_copy.icaweights;
ICA_CHANSIND=EEG_copy.icachansind;
% If channels were removed from copied dataset during preparation of ica, then remove
% those channels from original dataset as well before transferring ica weights.
EEG = eeg_checkset(EEG);
EEG = pop_select(EEG,'nochannel', ica_prep_badChans);
% Transfer the ICA weights of the copied dataset to the original dataset
EEG.icawinv=ICA_WINV;
EEG.icasphere=ICA_SPHERE;
EEG.icaweights=ICA_WEIGHTS;
EEG.icachansind=ICA_CHANSIND;
EEG = eeg_checkset(EEG);
%% STEP 10: Run adjust to find artifacted ICA components
badICs=[]; EEG_copy =[];
EEG_copy = EEG;
EEG_copy =eeg_regepochs(EEG_copy,'recurrence', 1, 'limits',[0 1], 'rmbase', [NaN], 'eventtype', '999'); % insert temporary marker 1 second apart and create epochs
EEG_copy = eeg_checkset(EEG_copy);
if size(EEG_copy.icaweights,1) == size(EEG_copy.icaweights,2)
if save_interim_result==1
badICs = adjusted_ADJUST(EEG_copy, [[output_location filesep 'ica_data' filesep] subses '_adjustReport']);
else
badICs = adjusted_ADJUST(EEG_copy, [[output_location filesep 'processed_data' filesep] strrep(datafile_names{run}, ext, '_adjustReport')]);
end
close all;
else % if rank is less than the number of electrodes, throw a warning message
warning('The rank is less than the number of electrodes. ADJUST will be skipped. Artefacted ICs will have to be manually rejected for this participant');
end
% Mark the bad ICs found by ADJUST
for ic=1:length(badICs)
EEG.reject.gcompreject(1, badICs(ic))=1;
EEG = eeg_checkset(EEG);
end
for i = 1 : run
total_ICs(i)=size(EEG.icasphere, 1);
if numel(badICs)==0
ICs_removed{i}='0';
else
ICs_removed{i}=num2str(double(badICs));
end
end
%% Save dataset after ICA, if saving interim results was preferred
if save_interim_result==1
if output_format==1
EEG = eeg_checkset(EEG);
EEG = pop_editset(EEG, 'setname', [subses '_mergedICA_eeg']);
EEG = pop_saveset(EEG, 'filename', [subses '_mergedICA_eeg.set'],'filepath', [output_location filesep 'ica_data' filesep ]); % save .set format
elseif output_format==2
save([output_location filesep 'ica_data' filesep subses '_mergedICA_eeg.mat'], 'EEG'); % save .mat format
end
end
%% STEP 11: Remove artifacted ICA components from data
all_bad_ICs=0;
ICs2remove=find(EEG.reject.gcompreject); % find ICs to remove
% If all ICs and bad, save data at this stage and ignore rest of the preprocessing for this run.
if numel(ICs2remove)==total_ICs(run)
all_bad_ICs=1;
warning(['No usable data for datafile']);
if output_format==1
EEG = eeg_checkset(EEG);
EEG = pop_editset(EEG, 'setname', [participant_label '_' session_label '_no_usable_data_all_bad_ICs']);
EEG = pop_saveset(EEG, 'filename', [participant_label '_' session_label '_no_usable_data_all_bad_ICs.set'],'filepath', [output_location filesep 'processed_data' filesep ]); % save .set format
elseif output_format==2
save([output_location filesep 'processed_data' filesep participant_label '_' session_label '_no_usable_data_all_bad_ICs.mat'], 'EEG'); % save .mat format
end
else
EEG = eeg_checkset( EEG );
EEG = pop_subcomp( EEG, ICs2remove, 0); % remove ICs from dataset
end
if all_bad_ICs==1
for i = 1 : run
total_epochs_before_artifact_rejection(i)=0;
total_epochs_after_artifact_rejection(i)=0;
total_channels_interpolated(i)=0;
end
end
%% STEP 11.5: Now seperate each task for remainder of processing
mEEG = deal(EEG);
for run = 1 : length(event_struct.file_names)
%% Step 11.75: Load task specific settings for processing
% 2. Enter the path of the folder where you want to save the processed data
s = grab_settings(event_struct.file_names(run), json_settings_file);
% 10. Do you want to remove/correct baseline?
remove_baseline = s.remove_baseline; % 0 = NO (no baseline correction), 1 = YES (baseline correction)
baseline_window = s.baseline_window; % baseline period in milliseconds (MS) [] = entire epoch
% 11. Do you want to remove artifact laden epoch based on voltage threshold?
voltthres_rejection = s.voltthres_rejection; % 0 = NO, 1 = YES
volt_threshold = s.volt_threshold; % lower and upper threshold (in uV)
%12. Do you want to interpolate epochs
interp_epoch = s.interp_epoch; % 0 = NO, 1 = YES.
frontal_channels = s.frontal_channels; % If you set interp_epoch = 1, enter the list of frontal channels to check (see manuscript for detail)
% recommended list for EGI 128 channel net: {'E1', 'E8', 'E14', 'E21', 'E25', 'E32', 'E17'}
%13. Do you want to interpolate the bad channels that were removed from data?
interp_channels = s.interp_channels; % 0 = NO (Do not interpolate), 1 = YES (interpolate missing channels)
% 14. Do you want to rereference your data?
rerefer_data = s.rerefer_data; % 0 = NO, 1 = YES
reref=s.reref; % Enter electrode name/s or number/s to be used for rereferencing
% For channel name/s enter, reref = {'channel_name', 'channel_name'};
% For channel number/s enter, reref = [channel_number, channel_number];
% For average rereference enter, reref = []; default is average rereference
%Specify that you are only interested in events for the given task
%EEG1 = pop_selectevent(mEEG, 'event', event_struct.indices{run},'deleteevents','on');
EEG = pop_selectevent(mEEG, 'input_file', event_struct.file_names{run}, 'deleteevents', 'on');
%% STEP 12: Segment data into fixed length epochs
if contains(event_struct.file_names{run}, 'MMN')
task = 'MMN';
elseif contains(event_struct.file_names{run}, 'RS')
task = 'RS';
elseif contains(event_struct.file_names{run}, 'VEP')
task = 'VEP';
elseif contains(event_struct.file_names{run}, 'FACE')
task = 'FACE';
end
Tasks(run) = string(task);
%Pull site information from scans.tsv (site) - TM 12/20/2024
%outEEGname = outEEG.setname;
try
%first try getting siteinfo from scans.tsv
sitepath = [bids_dir filesep participant_label filesep session_label];
sitetable = readtable([sitepath filesep participant_label '_' session_label '_scans.tsv'],"Filetype","text",'Delimiter','\t');
try
siteinfo=sitetable.site(contains(sitetable.filename,'eeg'));
siteinfo = siteinfo(1);
catch
error("Site data is missing!")
end
catch
%otherwise try getting site info from local PSCID
try
outEEGname = outEEG.setname;
siteinfo = outEEGname(3:5);
catch
error("Site data is missing locally!")
end
end
EEG = make_MADE_epochs(EEG, event_struct.file_names{run}, json_settings_file, task, siteinfo);
total_epochs_before_artifact_rejection(run)=EEG.trials;
%% STEP 13: Remove baseline
if remove_baseline==1
EEG = eeg_checkset( EEG );
EEG = pop_rmbase( EEG, baseline_window);
end
%% STEP 14: Artifact rejection
all_bad_epochs=0;
if voltthres_rejection==1 % check voltage threshold rejection
if interp_epoch==1 % check epoch level channel interpolation
chans=[]; chansidx=[];chans_labels2=[];
chans_labels2=cell(1,EEG.nbchan);
for i=1:EEG.nbchan
chans_labels2{i}= EEG.chanlocs(i).labels;
end
[chans,chansidx] = ismember(frontal_channels, chans_labels2);
frontal_channels_idx = chansidx(chansidx ~= 0);
badChans = zeros(EEG.nbchan, EEG.trials);
badepoch=zeros(1, EEG.trials);
if isempty(frontal_channels_idx)==1 % check whether there is any frontal channel in dataset to check
warning('No frontal channels from the list present in the data. Only epoch interpolation will be performed.');
else
% find artifaceted epochs by detecting outlier voltage in the specified channels list and remove epoch if artifacted in those channels
for ch =1:length(frontal_channels_idx)
EEG = pop_eegthresh(EEG,1, frontal_channels_idx(ch), volt_threshold(1), volt_threshold(2), EEG.xmin, EEG.xmax,0,0);
EEG = eeg_checkset( EEG );
EEG = eeg_rejsuperpose( EEG, 1, 1, 1, 1, 1, 1, 1, 1);
badChans(ch,:) = EEG.reject.rejglobal;
end
for ii=1:size(badChans, 2)
badepoch(ii)=sum(badChans(:,ii));
end
badepoch=logical(badepoch);
end
% If all epochs are artifacted, save the dataset and ignore rest of the preprocessing for this run.
if sum(badepoch)==EEG.trials || sum(badepoch)+1==EEG.trials
all_bad_epochs=1;
warning(['No usable data for datafile', event_struct.file_names{run}]);
if output_format==1
EEG = eeg_checkset(EEG);
EEG = pop_editset(EEG, 'setname', strrep(event_struct.file_names{run}, ext, '_no_usable_data_all_bad_epoch'));
EEG = pop_saveset(EEG, 'filename', strrep(event_struct.file_names{run}, ext, '_no_usable_data_all_bad_epoch.set'),'filepath', [output_location filesep 'processed_data' filesep ]); % save .set format
elseif output_format==2
save([[output_location filesep 'processed_data' filesep ] strrep(event_struct.file_names{run}, ext, '_no_usable_data_all_bad_epochs.mat')], 'EEG'); % save .mat format
end
else
EEG = pop_rejepoch( EEG, badepoch, 0);
EEG = eeg_checkset(EEG);
end
if all_bad_epochs==1
warning(['No usable data for datafile', event_struct.file_names{run}]);
else
% Interpolate artifacted data for all reaming channels
badChans = zeros(EEG.nbchan, EEG.trials);
% Find artifacted epochs by detecting outlier voltage but don't remove
for ch=1:EEG.nbchan
EEG = pop_eegthresh(EEG,1, ch, volt_threshold(1), volt_threshold(2), EEG.xmin, EEG.xmax,0,0);
EEG = eeg_checkset(EEG);
EEG = eeg_rejsuperpose(EEG, 1, 1, 1, 1, 1, 1, 1, 1);
badChans(ch,:) = EEG.reject.rejglobal;
end
tmpData = zeros(EEG.nbchan, EEG.pnts, EEG.trials);
for e = 1:EEG.trials
% Initialize variables EEGe and EEGe_interp;
EEGe = []; EEGe_interp = []; badChanNum = [];
% Select only this epoch (e)
EEGe = pop_selectevent( EEG, 'epoch', e, 'deleteevents', 'off', 'deleteepochs', 'on', 'invertepochs', 'off');
badChanNum = find(badChans(:,e)==1); % find which channels are bad for this epoch
EEGe_interp = eeg_interp(EEGe,badChanNum); %interpolate the bad channels for this epoch
tmpData(:,:,e) = EEGe_interp.data; % store interpolated data into matrix
end
EEG.data = tmpData; % now that all of the epochs have been interpolated, write the data back to the main file
% If more than 10% of channels in an epoch were interpolated, reject that epoch
badepoch=zeros(1, EEG.trials);
ii=1;
goodepoch = [];
for ei=1:EEG.trials
NumbadChan = badChans(:,ei); % find how many channels are bad in an epoch
if sum(NumbadChan) > round((10/100)*EEG.nbchan)% check if more than 10% are bad
badepoch (ei)= sum(NumbadChan);
else
goodepoch(ii)= ei; %LY 7/18/2023 for percent interpolated calc
ii=ii+1;
end
end
avginterp(run) = mean(sum(badChans(:,goodepoch),1));%LY 7/18/2023 for percent interpolated calc
stdinterp(run) = std(sum(badChans(:,goodepoch),1));%LY 7/18/2023 for percent interpolated calc
rangeinterp(run) = range(sum(badChans(:,goodepoch),1));%LY 7/18/2023 for percent interpolated calc
%avginterp_byfile(run) = avginterp;%LY 7/18/2023 for percent interpolated calc
badepoch=logical(badepoch);
end
% If all epochs are artifacted, save the dataset and ignore rest of the preprocessing for this run.
if sum(badepoch)==EEG.trials || sum(badepoch)+1==EEG.trials
all_bad_epochs=1;
warning(['No usable data for datafile', event_struct.file_names{run}]);
if output_format==1
EEG = eeg_checkset(EEG);
EEG = pop_editset(EEG, 'setname', strrep(event_struct.file_names{run}, ext, '_no_usable_data_all_bad_epochs'));
EEG = pop_saveset(EEG, 'filename', strrep(event_struct.file_names{run}, ext, '_no_usable_data_all_bad_epochs.set'),'filepath', [output_location filesep 'processed_data' filesep ]); % save .set format