Mne Bids Pipeline ((hot)) ❲No Survey❳

# Read events from BIDS events, event_id = mne.events_from_annotations(raw_clean) selected_events = ['stimulus/face', 'stimulus/car'] event_id_sel = k: v for k, v in event_id.items() if k in selected_events Create epochs epochs = mne.Epochs( raw_clean, events, event_id=event_id_sel, tmin=-0.2, # 200 ms pre-stimulus tmax=0.8, # 800 ms post-stimulus baseline=(-0.2, 0), reject=dict(eeg=100e-6), # reject epochs with >100 µV peak-to-peak preload=True, ) Drop bad epochs automatically epochs.drop_bad() print(f"Retained len(epochs) / len(epochs.events) epochs") Step 5: Sensor-Level Analysis – Evoked Responses # Compute evoked for each condition evoked_face = epochs['stimulus/face'].average() evoked_car = epochs['stimulus/car'].average() Plot butterfly plot evoked_face.plot_joint(title='Face condition') Difference wave evoked_diff = mne.combine_evoked([evoked_face, evoked_car], weights=[1, -1]) evoked_diff.plot_joint(title='Face - Car')

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

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

# 2. Notch filter (line noise) if notch: raw.notch_filter(notch, fir_design='firwin', verbose=True)

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, ) # Read events from BIDS events, event_id = mne

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

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) Apply to evoked data stc = mne

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: