MNE is a community-driven software package designed for for processing electroencephalography (EEG) and magnetoencephalography (MEG) data providing comprehensive tools and workflows for:

  1. Preprocessing
  2. Source estimation
  3. Time–frequency analysis
  4. Statistical testing
  5. Estimation of functional connectivity
  6. Applying machine learning algorithms
  7. Visualization of sensor- and source-space data

MNE includes a comprehensive Python package (provided under the simplified BSD license), 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:

>>> import mne  
>>> raw ='raw.fif', preload=True)  # load data  
>>>['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(, 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(, 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()  

The MNE development is supported by National Institute of Biomedical Imaging and Bioengineering grants 5R01EB009048 and P41EB015896 (Center for Functional Neuroimaging Technologies) as well as NSF awards 0958669 and 1042134. It has been supported by the NCRR Center for Functional Neuroimaging Technologies P41RR14075-06, the NIH grants 1R01EB009048-01, R01 EB006385-A101, 1R01 HD40712-A1, 1R01 NS44319-01, and 2R01 NS37462-05, ell as by Department of Energy under Award Number DE-FG02-99ER62764 to The MIND Institute.