eegmp_analysis/code/z_eegmp_old_new_ERP.m

function z_eegmp_old_new_ERP(rootpath, id)

%% paths
if ismac
    currentFile = mfilename('fullpath');
    [pathstr,~,~] = fileparts(currentFile);
    cd(fullfile(pathstr,'..'))
    rootpath = pwd;
    id = 'sub-011';
end

pn.data_eeg = fullfile(rootpath, '..', 'eegmp_preproc', 'data', 'outputs', 'eeg');
pn.data_erp = fullfile(rootpath, 'data', 'test'); mkdir(pn.data_erp);
pn.tools = fullfile(rootpath, '..', 'eegmp_preproc', 'tools');
    addpath(fullfile(pn.tools, 'fieldtrip')); ft_defaults

%% load data_eeg

% load preprocessed eeg data_eeg
load(fullfile(pn.data_eeg, [id,'_task-xxxx_eeg_art.mat']), 'data_eeg', 'events');

%% further preprocessing

% apply notch filter

cfg = [];
cfg.dftfilter = 'yes';
data_eeg = ft_preprocessing(cfg, data_eeg);

%% CSD transform
    
% csd_cfg = [];
% csd_cfg.method = 'spline';
% data_eeg = ft_scalpcurrentdensity(csd_cfg, data_eeg);

%% single-trial baseline-correction for ERPs

time = data_eeg.time{1};

data_eeg_bl = data_eeg;

for indTrial = 1:numel(data_eeg_bl.trial)
    curbl = squeeze(nanmean(data_eeg_bl.trial{indTrial}(:,time>-.2 & time<0),2));
    data_eeg_bl.trial{indTrial} = data_eeg_bl.trial{indTrial}-repmat(curbl,1,numel(time));
end

%% split tfr and time series data by condition, average across trials

cfg = [];
cfg.trials = ismember(events.old, 'old');
erp_bl.old = ft_timelockanalysis(cfg, data_eeg_bl);

cfg = [];
cfg.trials = ismember(events.old, 'new');
erp_bl.new = ft_timelockanalysis(cfg, data_eeg_bl);

%% save data

save(fullfile(pn.data_erp, [id, '_erp_bl.mat']), 'erp_bl');