MNE is a community-driven software package designed for processing electroencephalography (EEG) and magnetoencephalography (MEG) data providing comprehensive tools and workflows for (among other things):
MNE includes a comprehensive Python package supplemented by tools compiled from C code for the LINUX and Mac OSX operating systems, as well as a MATLAB toolbox.
From raw data to source estimates in about 30 lines of code (try it yourself!):
>>> import mne >>> raw = mne.io.read_raw_fif('raw.fif', preload=True) # load data >>> raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels >>> raw.filter(l_freq=None, h_freq=40.0) # low-pass filter data >>> # Extract epochs and save them: >>> picks = mne.pick_types(raw.info, meg=True, eeg=True, eog=True, >>> exclude='bads') >>> events = mne.find_events(raw) >>> reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6) >>> epochs = mne.Epochs(raw, events, event_id=1, tmin=-0.2, tmax=0.5, >>> proj=True, picks=picks, baseline=(None, 0), >>> preload=True, reject=reject) >>> # Compute evoked response and noise covariance >>> evoked = epochs.average() >>> cov = mne.compute_covariance(epochs, tmax=0) >>> evoked.plot() # plot evoked >>> # 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()
MNE development is driven by extensive contributions from the community. Direct financial support for the project has been provided by: