Pipeline — Mne Bids

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 :

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 mne bids pipeline

Run in parallel:

pip install mne mne-bids pybv from pathlib import Path import mne from mne_bids import BIDSPath, write_raw_bids, make_dataset_description Define your project root bids_root = Path('/path/to/your/bids_dataset') bids_root.mkdir(exist_ok=True) Create a dataset description (required for BIDS) make_dataset_description( path=bids_root, name="My MEG/EEG Study", authors=["Your Name", "Collaborator"], dataset_doi="", funding="Grant #", ) Define a subject and session subject_id = '001' session_id = '01' # optional task = 'visual' Convert a single raw file (e.g., BrainVision .vhdr) raw_path = Path('/raw_data/sub-001/session_1/eeg.vhdr') bids_path = BIDSPath( subject=subject_id, session=session_id, task=task, suffix='eeg', root=bids_root, ) Write to BIDS (copies and anonymizes) raw = mne.io.read_raw_brainvision(raw_path, preload=False) write_raw_bids( raw, bids_path, overwrite=False, verbose=True, ) return raw raw_clean = preprocess_raw(raw) 5