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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 :
from mne_bids import read_raw_bids bids_path = BIDSPath( subject='001', session='01', task='visual', suffix='eeg', root=bids_root, ) mne bids pipeline
# 2. Notch filter (line noise) if notch: raw.notch_filter(notch, fir_design='firwin', verbose=True) return raw raw_clean = preprocess_raw(raw) 5
Run in parallel:
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) Inverse operator (dSPM or MNE) inverse_operator = mne
Save source estimates in BIDS derivatives using mne-bids :
