Mne Bids Pipeline !new! -

with open(args.config, 'r') as f: config = yaml.safe_load(f) main(args.subject, config)

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. mne bids pipeline

For group analysis, save evoked data in BIDS-derivatives:

return raw raw_clean = preprocess_raw(raw) 5. ICA for artifact removal (eye blinks, heartbeats) ica = mne.preprocessing.ICA(n_components=20, random_state=42) ica.fit(raw_clean.copy().filter(1, 30)) # ICA works better on high-passed Identify EOG artifacts eog_indices, eog_scores = ica.find_bads_eog(raw_clean, ch_name='Fp1') ica.exclude = eog_indices raw_clean = ica.apply(raw_clean) Step 4: Epoching and Baseline Correction Events are automatically read from *_events.tsv : with open(args

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

from mne_bids import write_anat write_anat(bids_root, subject='001', t1w_anat='sub-001_T1w.nii.gz') Assuming you have one evoked response per subject per condition: ICA for artifact removal (eye blinks, heartbeats) ica = mne

# 3. Interpolate bad channels (defined in BIDS channels.tsv) # MNE automatically reads 'status' column as bad if 'bad' is present. raw.interpolate_bads(reset_bads=True)