|
@@ -0,0 +1,99 @@
|
|
|
|
+function a1_prepare_preprocessing(id, rootpath)
|
|
|
|
+
|
|
|
|
+% prepare (i.e. filter + downsample data) for ICA1
|
|
|
|
+
|
|
|
|
+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
|
|
|
|
+
|
|
|
|
+%% define IDs for visual screening
|
|
|
|
+
|
|
|
|
+% N = 33
|
|
|
|
+IDs = tdfread(fullfile(pn.eeg_BIDS, 'participants.tsv'));
|
|
|
|
+IDs = cellstr(IDs.participant_id);
|
|
|
|
+
|
|
|
|
+id = str2num(id);
|
|
|
|
+
|
|
|
|
+% 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'};
|
|
|
|
+
|
|
|
|
+% 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.refmethod = 'avg';
|
|
|
|
+cfg_preproc.refchannel = {'M1', 'M2'};
|
|
|
|
+cfg_preproc.implicitref = 'POz';
|
|
|
|
+
|
|
|
|
+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 if it does not exist yet (otherwise we risk overwriting manual segs)
|
|
|
|
+if ~exist(fullfile(pn.history, [IDs{id}, '_task-xxxx_config.mat']),'file')
|
|
|
|
+ save(fullfile(pn.history, [IDs{id}, '_task-xxxx_config.mat']),'config');
|
|
|
|
+end
|
|
|
|
+% clear variables
|
|
|
|
+clear cfg_* config data_eeg
|
|
|
|
+
|
|
|
|
+end
|