Community-driven software for processing time-resolved neural signals including electroencephalography (EEG) and magnetoencephalography (MEG), offering comprehensive data analysis tools for Windows, OSX, and Linux:
From raw data to source estimates in about 20 lines of code (try it in an experimental online demo!):
>>> import mne
>>> raw = mne.io.read_raw_fif('raw.fif') # load data
>>> raw.info['bads'] = ['MEG 2443', 'EEG 053'] # mark bad channels
>>> raw.filter(l_freq=None, h_freq=40.0) # low-pass filter
>>> events = mne.find_events(raw, 'STI014') # extract events and epoch data
>>> epochs = mne.Epochs(raw, events, event_id=1, tmin=-0.2, tmax=0.5,
>>> reject=dict(grad=4000e-13, mag=4e-12, eog=150e-6))
>>> evoked = epochs.average() # compute evoked
>>> evoked.plot() # butterfly plot the evoked data
>>> cov = mne.compute_covariance(epochs, tmax=0, method='shrunk')
>>> 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 operator
>>> stc = mne.minimum_norm.apply_inverse(
>>> evoked, inv, lambda2=1. / 9., method='dSPM') # apply it
>>> stc_fs = stc.morph('fsaverage') # morph to fsaverage
>>> stc_fs.plot() # plot source data on fsaverage's brain
Direct financial support for MNE has been provided by the United States:
And France: