raw = read_raw_bids(bids_path, verbose=True) raw.load_data() # now in memory - Channel locations (from .tsv) - Events (from events.tsv) - Bad channels (from channels.tsv) print(raw) Step 3: Preprocessing Pipeline A typical preprocessing pipeline in MNE for BIDS data:

# 4. Set average reference (EEG) if 'eeg' in raw: raw.set_eeg_reference('average', projection=False)

Save source estimates in BIDS derivatives using mne-bids :

src = mne.setup_source_space('sub-001', spacing='oct6', subjects_dir=subjects_dir) fwd = mne.make_forward_solution( raw.info, trans=None, src=src, bem=bem_sol, meg=False, eeg=True ) 4. Inverse operator (dSPM or MNE) inverse_operator = mne.minimum_norm.make_inverse_operator( epochs.info, fwd, cov, loose=0.2, depth=0.8 ) 5. Apply to evoked data stc = mne.minimum_norm.apply_inverse( evoked_face, inverse_operator, lambda2=1/9., method='dSPM' ) Plot on cortical surface stc.plot(subject='sub-001', subjects_dir=subjects_dir, initial_time=0.1)

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, )