MNE is an academic software package that aims to provide data analysis pipelines encompassing all phases of M/EEG data processing.
MNE started as tool written in C by Matti Hämäläinen while at MGH in Boston. MNE was then extended with the Python programming language to implement nearly all MNE-C’s functionality, offer transparent scripting, and extend MNE-C’s functionality considerably.
A basic Matlab toolbox is also available mostly to allow reading and write MNE files. The sister MNE with CPP project aims to provide modular and open-source tools for acquisition, visualization, and analysis.
This package is based on the FIF file format from Neuromag. But, it can read and convert CTF, BTI/4D, KIT and various EEG formats to FIF (see IO functions).
If you have been using MNE-C, there is no need to convert your fif files to a new system or database – MNE-Python works nicely with the historical fif files.
- Raw data visualization to visualize recordings (see Examples Gallery for more).
- Epoching: Define epochs, baseline correction, handle conditions etc.
- Averaging to get Evoked data.
- Compute SSP projectors to remove ECG and EOG artifacts.
- Compute ICA to remove artifacts or select latent sources.
- Maxwell filtering to remove environmental noise.
- Boundary Element Modeling: single and three-layer BEM model creation and solution computation.
- Forward modeling: BEM computation and mesh creation (see The forward solution).
- Linear inverse solvers (dSPM, sLORETA, MNE, LCMV, DICS).
- Sparse inverse solvers (L1/L2 mixed norm MxNE, Gamma Map, Time-Frequency MxNE, RAP-MUSIC).
- Connectivity estimation in sensor and source space.
- Visualization of sensor and source space data
- Time-frequency analysis with Morlet wavelets (induced power, intertrial coherence, phase lock value) also in the source space.
- Spectrum estimation using multi-taper method.
- Mixed Source Models combining cortical and subcortical structures.
- Dipole Fitting
- Decoding multivariate pattern analysis of M/EEG topographies.
- Compute contrasts between conditions, between sensors, across subjects etc.
- Non-parametric statistics in time, space and frequency (including cluster-level).
- Scripting (batch and parallel computing)
Short answer is No! You can also do:
- detect heart beat QRS component
- detect eye blinks and EOG artifacts
- compute SSP projections to remove ECG or EOG artifacts
- compute Independent Component Analysis (ICA) to remove artifacts or select latent sources
- estimate noise covariance matrix from Raw and Epochs
- visualize cross-trial response dynamics using epochs images
- compute forward solutions
- estimate power in the source space
- estimate connectivity in sensor and source space
- morph stc from one brain to another for group studies
- compute mass univariate statistics base on custom contrasts
- visualize source estimates
- export raw, epochs, and evoked data to other python data analysis libraries e.g. pandas
- Raw movement compensation as you would do with Elekta Maxfilter™
- and many more things ...