eegmp_preproc/code/a5_segmentation_raw_data/a5_segmentation_raw_data.m

function a5_segmentation_raw_data(id, rootpath)

if ismac % run if function is not pre-compiled
    id = '1'; % test for example subject
    currentFile = mfilename('fullpath');
    [pathstr,~,~] = fileparts(currentFile);
    cd(fullfile(pathstr,'..', '..'))
    rootpath = pwd;
end

% inputs
pn.eeg_BIDS = fullfile(rootpath, 'data', 'inputs', 'rawdata', 'eeg_BIDS');
pn.channel_locations = fullfile(rootpath, 'data', 'inputs', 'rawdata', 'channel_locations');
pn.events = fullfile(rootpath, 'data', 'inputs', 'rawdata', 'events');
pn.tools = fullfile(rootpath, 'tools');
% outputs
pn.eeg_ft = fullfile(rootpath, 'data', 'outputs', 'eeg');
pn.history = fullfile(rootpath, 'data', 'outputs', 'history');

if ismac % run if function is not pre-compiled
     addpath(fullfile(pn.tools, 'fieldtrip')); ft_defaults;
     addpath(fullfile(pn.tools, 'helpers'));
end

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

id = str2num(id);

display(['processing ID ' num2str(IDs{id})]);
    
%%  load raw data

if ~exist(fullfile(pn.eeg_ft, [IDs{id}, '_task-xxxx_eeg_seg.mat']),'file')

    % load config
    config = [];
    load(fullfile(pn.history, [IDs{id}, '_task-xxxx_config.mat']),'config');

    %% load events file
    
    events = readtable(fullfile(pn.events, ['EMP',IDs{id}(end-1:end),'_events.csv']));
    
    %% generate segmentation structure

    % load marker file
    mrk = config.mrk;
    
    % generate trial structure
    mrk_val = {mrk(1,:).value};
    indOnset = find(strcmp(mrk_val(:),'boundary')) + 1;
    mrk_val = mrk_val(indOnset:end);

    % Stim trials
    fsample = 512;
    trl = zeros(length(mrk_val),3);
    TOI1 = 1*fsample; % segmentation before trigger (s x fsample)
    TOI2 = 2*fsample; % segmentation following trigger (s x fsample)

    for j = 1:size(trl,1)
        trl(j,1) = mrk(1,indOnset+j-1).sample - TOI1;
        trl(j,2) = mrk(1,indOnset+j-1).sample + TOI2;
        trl(j,3) = -TOI1; % offset
    end; clear j

    % add trial structure to config
    config.trl = trl;

    %% load, filter, and re-reference raw data

    % define reading & preprocessing parameters
    % read in all data first

    % load data
    cfg           = [];
    cfg.datafile  = ...
        fullfile(pn.eeg_BIDS, IDs{id}, 'eeg', [IDs{id}, '_task-xxxx_eeg.eeg']);
    cfg.trl         = config.trl;
    cfg.channel     = {'all'};

    data_eeg            = ft_preprocessing(cfg);

    %% reref & filter EEG data
    
    cfg             = [];

    cfg.continuous  = 'yes';
    cfg.demean      = 'yes';

    cfg.reref       = 'yes';
    cfg.refmethod   = 'avg';
    cfg.refchannel  = {'M1', 'M2'};
    cfg.implicitref = 'POz';

    cfg.hpfilter    = 'yes';
    cfg.hpfreq      = .2;
    cfg.hpfiltord   = 4;
    cfg.hpfilttype  = 'but';

    cfg.lpfilter    = 'yes';
    cfg.lpfreq      = 125;
    cfg.lpfiltord   = 4;
    cfg.lpfilttype  = 'but';

    % get data
    data_eeg = ft_preprocessing(cfg, data_eeg);
    flt = cfg;

    % clear cfg structure
    clear cfg

    %% resample to 500 Hz

    % define settings for resampling
    cfg.resamplefs = 500;
    cfg.detrend    = 'no';
    cfg.feedback   = 'no';
    cfg.trials     = 'all';
    data_eeg    = ft_resampledata(cfg,data_eeg);

    resample = cfg;
    % clear variables
    clear cfg

    % update config
    config.seg.flt = flt;
    config.seg.resample = resample;

    %% save eeg data and history
    
    save(fullfile(pn.eeg_ft, [IDs{id}, '_task-xxxx_eeg_seg.mat']),'data_eeg', 'events');
    save(fullfile(pn.history, [IDs{id}, '_task-xxxx_config.mat']),'config');

end % exist