eegmp_preproc/code/a1_prepare_preprocessing.m

%% 01_prepare_preprocessing

% prepare (i.e. filter + downsample data) for ICA1

%% initialize

restoredefaultpath;
clear all; close all; pack; clc;

%% pathdef

currentFile = mfilename('fullpath');
[pathstr,~,~] = fileparts( currentFile );
cd(pathstr)

% inputs
pn.eeg_BIDS = fullfile('..', 'data', 'inputs', 'rawdata', 'eeg_BIDS');
pn.channel_locations = fullfile('..', 'data', 'inputs', 'rawdata', 'channel_locations');
pn.events = fullfile('..', 'data', 'inputs', 'rawdata', 'events');
% outputs
pn.eeg_ft = fullfile('..', 'data', 'outputs', 'eeg'); if ~exist(pn.eeg_ft); mkdir(pn.eeg_ft); end
pn.history = fullfile('..', 'data', 'outputs', 'history'); if ~exist(pn.history); mkdir(pn.history); end

% add fieldtrip toolbox
addpath(fullfile('..', 'tools', 'fieldtrip')); ft_defaults;

%% define IDs for preprocessing

% N = 33
IDs = tdfread(fullfile(pn.eeg_BIDS, 'participants.tsv'));
IDs = cellstr(IDs.participant_id);

for id = 1:length(IDs)

if ~exist(fullfile(pn.eeg_ft, [IDs{id}, '_task-xxxx_eeg_raw.mat']),'file')
    
    % load data
    cfg_preproc           = [];
    cfg_preproc.datafile  = ...
        fullfile(pn.eeg_BIDS, IDs{id}, 'eeg', [IDs{id}, '_task-xxxx_eeg.eeg']);

    % load header & event information
    config              = ft_read_header(cfg_preproc.datafile);
    config.data_file    = cfg_preproc.datafile;
    config.mrk          = ft_read_event(cfg_preproc.datafile);

    % define reading & preprocessing parameters
    cfg_preproc.channel     = {'all'};
    cfg_preproc.implicitref = 'REF'; % recover implicit reference

    % get all data first, then apply specific steps only for subsets
    data_eeg = ft_preprocessing(cfg_preproc);

    %% preprocessing

    cfg_preproc               = [];                                 
    cfg_preproc.channel       = {'all'};

    cfg_preproc.continuous  = 'yes';
    cfg_preproc.demean      = 'yes';

    cfg_preproc.reref       = 'yes';
    cfg_preproc.refchannel  = {'M1', 'M2'};
    cfg_preproc.implicitref = 'A2';

    cfg_preproc.hpfilter    = 'yes';
    cfg_preproc.hpfreq      = 1;
    cfg_preproc.hpfiltord   = 4;
    cfg_preproc.hpfilttype  = 'but';

    cfg_preproc.lpfilter    = 'yes';
    cfg_preproc.lpfreq      = 100;
    cfg_preproc.lpfiltord   = 4;
    cfg_preproc.lpfilttype  = 'but';

    data_eeg = ft_preprocessing(cfg_preproc, data_eeg);
    
    % define settings for resampling
    cfg_resample.resamplefs = 250;
    cfg_resample.detrend    = 'no';
    cfg_resample.feedback   = 'no';
    cfg_resample.trials     = 'all';

    data_eeg = ft_resampledata(cfg_resample,data_eeg);
    
    % change data precision to single
    for t = 1:length(data_eeg.trial)
        data_eeg.trial{t} = single(data_eeg.trial{t});
    end; clear t

    %% save outputs

    save(fullfile(pn.eeg_ft, [IDs{id}, '_task-xxxx_eeg_raw.mat']),'data_eeg');
    % save config
    save(fullfile(pn.history, [IDs{id}, '_task-xxxx_config.mat']),'config');
    % clear variables
    clear cfg_* config data_eeg
    
end
end; clear id;