ephypype.pipelines.create_pipeline_preproc_meeg

ephypype.pipelines.create_pipeline_preproc_meeg(main_path, pipeline_name='preproc_meeg_pipeline', data_type='fif', l_freq=1, h_freq=150, down_sfreq=None, is_ICA=True, variance=None, n_components=None, ECG_ch_name='', EoG_ch_name='', reject=None, is_set_ICA_components=False, mapnode=False, n_comp_exclude=[], is_sensor_space=True, montage=None, misc=None, bipolar=None, ch_new_names=None)

Preprocessing pipeline.

Parameters:

main_pathstr

main path to of the pipeline

pipeline_name: str (default ‘preproc_meeg’)

name of the pipeline

data_type: str (default ‘fif’)

data type: MEG (‘fif’, ‘ds’) or EEG (‘eeg’) data

l_freq: float (default 1)

low cut-off frequency in Hz

h_freq: float (default 150)

high cut-off frequency in Hz

down_sfreq: float (default 300)

sampling frequency at which the data are downsampled

is_ICAboolean (default True)

if True apply ICA to automatically remove ECG and EoG artifacts

variance: float (default 0.95)

float between 0 and 1: the ICA components will be selected by the cumulative percentage of explained variance

n_components: int

number of ICA components

ECG_ch_name: str (default ‘’)

the name of ECG channels

EoG_ch_name: str (default ‘’)

the name of EoG channels

reject: dict | None

rejection parameters based on peak-to-peak amplitude. Valid keys are ‘grad’ | ‘mag’ | ‘eeg’ | ‘eog’ | ‘ecg’. If reject is None then no rejection is done

is_set_ICA_components: boolean (default False)

set to True if we had the ICA of the raw data, checked the Report and want to exclude some ICA components based on their topographies and time series if True, we have to fill the dictionary variable n_comp_exclude

n_comp_exclude: dict

if is_set_ICA_components=True, it has to be a dict containing for each subject and for each session the components to be excluded

is_sensor_space: boolean (default True)

True if we perform the analysis in sensor space and we use the pipeline as lego with the connectivity or inverse pipeline

raw_file (inputnode): str

path to raw meg data in fif format

subject_id (inputnode): str

subject id

Returns:

pipelineinstance of Workflow