Table 1.

From raw data to dSPM source estimates in less than 30 lines of code.

import mne # load data raw = mne.fiff.Raw('raw.fif' ,preload=True) raw.info ['bads'] = ['MEG 2443', 'EEG 053'] #mark bad channels # low-pass filter data raw.filter (l_freq=None, h_freq=40.0) # extract epochs and save them picks = mne.fiff.pick_types (raw.info, meg=True, eeg=True, eog=True, exclude='bads') events = mne.find_events (raw) epochs = mne.Epochs (raw, events, event_id=1, tmin=−0.2, tmax=0.5, proj=True, picks=picks, baseline=(None, 0), preload=True, reject=dict (grad=4000e−13, mag=4e−12, eog=150e−6)) # compute evoked response and noise covariance,and plot evoked evoked = epochs.average () cov = mne.compute_covariance (epochs, tmax=0) evoked.plot () # compute inverse operator fwd_fname = 'sample_audvis−meg−eeg−oct−6−fwd.fif' fwd = mne.read_forward_solution(fwd_fname,surf_ori=True) inv = mne.minimum_norm.make_inverse_operator(raw.info, fwd, cov, loose=0.2) # compute inverse solution stc = mne.minimum_norm.apply_inverse(evoked, inv, lambda2=1./9., method='dSPM') # morph it to average brain for group study and plot it stc_avg = mne.morph_data ('sample', 'fsaverage', stc, 5, smooth=5) stc_avg.plot ()