Mne Bids Pipeline ✨

t_obs, clusters, p_values, H0 = cluster_stats Use a configuration file (YAML) # config.yaml subjects: ['001', '002', '003'] task: 'visual' preprocessing: l_freq: 0.1 h_freq: 40 notch: 50 epochs: tmin: -0.2 tmax: 0.8 baseline: [-0.2, 0] Python script with argparse import yaml, argparse from mne_bids import BIDSPath, read_raw_bids def main(subject, config): # load config # run pipeline for one subject pass

if == ' main ': parser = argparse.ArgumentParser() parser.add_argument('--subject', required=True) parser.add_argument('--config', default='config.yaml') args = parser.parse_args() mne bids pipeline

import mne def preprocess_raw(raw, l_freq=0.1, h_freq=40, notch=50): """ Apply standard EEG preprocessing. Adjust parameters for MEG (e.g., high-pass 1 Hz, low-pass 100 Hz). """ # 1. Filter (bandpass) raw.filter(l_freq, h_freq, fir_design='firwin', verbose=True) t_obs, clusters, p_values, H0 = cluster_stats Use a

Save source estimates in BIDS derivatives using mne-bids : Filter (bandpass) raw

from mne_bids import read_raw_bids bids_path = BIDSPath( subject='001', session='01', task='visual', suffix='eeg', root=bids_root, )

Introduction The Brain Imaging Data Structure (BIDS) has revolutionized neuroimaging by providing a standardized way to organize, describe, and share data. For M/EEG research, the mne-bids package bridges the gap between raw data in proprietary formats (e.g., BrainVision, EDF, FIF) and the powerful analysis tools of MNE-Python.