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- 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
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