Mne Bids Pipeline _hot_ Now

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)

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, )

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: mne bids pipeline

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

# 4. Set average reference (EEG) if 'eeg' in raw: raw.set_eeg_reference('average', projection=False) import mne def preprocess_raw(raw, l_freq=0

Run in parallel:

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

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 :