Documentation

This is where you can learn about all the things you can do with MNE. It contains background information and tutorials for taking a deep-dive into the techniques that MNE-python covers. You’ll find practical information on how to use these methods with your data, and in many cases some high-level concepts underlying these methods.

There are also examples, which contain a short use-case to highlight MNE-functionality and provide inspiration for the many things you can do with this package. You can also find a gallery of these examples in the examples gallery.

See the links below for an introduction to MNE-python, or click one of the sections on this page to see more.

Introduction to MNE and Python

Getting started

• Installation — overview
• Advanced setup of MNE-Python

MNE basics

• MNE quickstart and background
• The typical M/EEG workflow
• What’s new
• Python API Reference
• Glossary
• Examples Gallery
• Command line tools using Python
• Configuring MNE python
• Publications by users
• Frequently Asked Questions (FAQ)

More help

• Cite MNE

• Mailing list for analysis talk

• GitHub issues for requests and bug reports

• Gitter to chat with devs

Data structures and containers

• The Raw data structure: continuous data
• The Epochs data structure: epoched data
• The Evoked data structure: evoked/averaged data
• The SourceEstimate data structure
• The Info data structure
• The Events and Annotations data structures

Data I/O and datasets

Getting your data into MNE

• Importing data into MNE
• Creating MNE’s data structures from scratch
• Pandas querying and metadata with Epochs objects
• Modifying data in-place
• Working with ECoG data
• Memory-efficient IO
• Migrating from EEGLAB

Examples

• Getting averaging info from .fif files
• Creating MNE objects from data arrays
• Reading and writing raw files
• Reading epochs from a raw FIF file
• Reading an event file
• Reading and writing an evoked file
• Reading/Writing a noise covariance matrix

Working with public datasets

• Datasets
• Brainstorm auditory tutorial dataset
• Brainstorm CTF phantom dataset tutorial
• Brainstorm Elekta phantom dataset tutorial
• 4D Neuroimaging/BTi phantom dataset tutorial
• Sleep stage classification from polysomnography (PSG) data
• Brainstorm raw (median nerve) dataset
• Optically pumped magnetometer (OPM) data
• From raw data to dSPM on SPM Faces dataset

Preprocessing (filtering, SSP, ICA, Maxwell filtering, ...)

Background

• Background information on filtering
• Signal-Space Projection (SSP)
• Independent Component Analysis (ICA)
• Maxwell filtering
• Bad channel repair via interpolation

Preprocessing your data

• Introduction to artifacts and artifact detection
• Filtering and resampling data
• Rejecting bad data (channels and segments)
• Artifact Correction with SSP
• Artifact Correction with ICA
• Artifact correction with Maxwell filter

Examples

• Define target events based on time lag, plot evoked response
• Show EOG artifact timing
• Find ECG artifacts
• Find EOG artifacts
• Visualize subject head movement
• Compare the different ICA algorithms in MNE
• Interpolate bad channels for MEG/EEG channels
• Maxwell filter data with movement compensation
• Re-referencing the EEG signal
• Resampling data
• Compute ICA components on epochs
• Shifting time-scale in evoked data
• Remap MEG channel types
• XDAWN Denoising

Visualization

• Visualize Raw data
• Visualize Epochs data
• Visualize Evoked data
• Visualize Source time courses
• Plotting whitened data
• Getting started with MNE web report

Examples

• Make an MNE-Report with a Slider
• How to convert 3D electrode positions to a 2D image.
• Visualize channel over epochs as an image
• Plotting EEG sensors on the scalp
• Plotting topographic maps of evoked data
• Whitening evoked data with a noise covariance
• Plotting sensor layouts of MEG systems
• Plot a cortical parcellation
• Show noise levels from empty room data
• Sensitivity map of SSP projections
• Compare evoked responses for different conditions
• Plot custom topographies for MEG sensors
• Cross-hemisphere comparison

Time- and frequency-domain analyses

Tutorials

• Overview of MEG/EEG analysis with MNE-Python
• Epoching and averaging (ERP/ERF)
• EEG processing and Event Related Potentials (ERPs)
• Frequency and time-frequency sensors analysis
• Spectral and Time-frequency Analysis

Examples

• Compute the power spectral density of raw data
• Compute Power Spectral Density of inverse solution from single epochs
• Compute power and phase lock in label of the source space
• Compute power spectrum densities of the sources with dSPM
• Compute induced power in the source space with dSPM
• Temporal whitening with AR model
• Explore event-related dynamics for specific frequency bands
• Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)
• Compute a cross-spectral density (CSD) matrix

Source-level analysis

Background

• FreeSurfer integration with MNE-Python
• The forward solution
• The minimum-norm current estimates
• Morphing source estimates: Moving data from one brain to another

Getting data to source space

• Source alignment and coordinate frames
• Head model and forward computation
• Computing a covariance matrix
• EEG forward operator with a template MRI
• Source localization with MNE/dSPM/sLORETA/eLORETA
• Computing various MNE solutions
• Source localization with equivalent current dipole (ECD) fit
• Corrupt known signal with point spread
• The role of dipole orientations in distributed source localization
• DICS for power mapping

Forward examples

• Decimating scalp surface
• Display sensitivity maps for EEG and MEG sensors
• Generate a left cerebellum volume source space
• Use source space morphing

Inverse examples

• VectorView and OPM resting state datasets
• Compute MNE-dSPM inverse solution on single epochs
• Compute sLORETA inverse solution on raw data
• Compute MNE-dSPM inverse solution on evoked data in volume source space
• Demonstrate impact of whitening on source estimates
• Source localization with a custom inverse solver
• Compute source power using DICS beamfomer
• Compute a sparse inverse solution using the Gamma-Map empirical Bayesian method
• Extracting time course from source_estimate object
• Generate a functional label from source estimates
• Extracting the time series of activations in a label
• Compute LCMV beamformer on evoked data
• Compute LCMV inverse solution in volume source space
• Compute MNE inverse solution on evoked data in a mixed source space
• Compute sparse inverse solution with mixed norm: MxNE and irMxNE
• Compute cross-talk functions (CTFs) for labels for MNE/dSPM/sLORETA
• Compute point-spread functions (PSFs) for MNE/dSPM/sLORETA
• Morph surface source estimate
• Morph volumetric source estimate
• Compute Rap-Music on evoked data
• Reading an STC file
• Reading an inverse operator
• Reading a source space from a forward operator
• Plot an estimate of data SNR
• Time-frequency beamforming using DICS
• Time-frequency beamforming using LCMV
• Compute MxNE with time-frequency sparse prior
• Plotting the full MNE solution

Simulation examples

• Generate simulated evoked data
• Generate simulated raw data
• Simulate raw data using subject anatomy

Statistics

Background

• Statistical inference

Sensor Space

• Spatiotemporal permutation F-test on full sensor data
• Non-parametric 1 sample cluster statistic on single trial power
• Non-parametric between conditions cluster statistic on single trial power
• Visualising statistical significance thresholds on EEG data

Examples

• FDR correction on T-test on sensor data
• Permutation F-test on sensor data with 1D cluster level
• Permutation T-test on sensor data
• Analysing continuous features with binning and regression in sensor space
• Regression on continuous data (rER[P/F])

Source Space

• Mass-univariate twoway repeated measures ANOVA on single trial power
• 2 samples permutation test on source data with spatio-temporal clustering
• Repeated measures ANOVA on source data with spatio-temporal clustering
• Permutation t-test on source data with spatio-temporal clustering

Machine learning (decoding, encoding, MVPA)

Decoding

• Decoding (MVPA)

Examples

• Representational Similarity Analysis
• Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
• Decoding in time-frequency space data using the Common Spatial Pattern (CSP)
• Decoding source space data
• Continuous Target Decoding with SPoC
• Decoding sensor space data with generalization across time and conditions
• Analysis of evoked response using ICA and PCA reduction techniques
• XDAWN Decoding From EEG data
• Compute effect-matched-spatial filtering (EMS)
• Linear classifier on sensor data with plot patterns and filters

Encoding

• Spectro-temporal receptive field (STRF) estimation on continuous data

Examples

• Receptive Field Estimation and Prediction

Connectivity

Examples

• Compute seed-based time-frequency connectivity in sensor space
• Compute mixed source space connectivity and visualize it using a circular graph
• Compute coherence in source space using a MNE inverse solution
• Compute envelope correlations in source space
• Compute envelope correlations in volume source space
• Compute full spectrum source space connectivity between labels
• Compute source space connectivity and visualize it using a circular graph
• Compute Phase Slope Index (PSI) in source space for a visual stimulus
• Compute all-to-all connectivity in sensor space

Realtime

All realtime functionality has migrated to mne_realtime.

MNE-C and MNE-MATLAB

MNE-C

• Getting started with MNE Unix command line tools
• C API Reference
• Interactive analysis with mne_analyze
• Browsing raw data with mne_browse_raw
• The forward solution
• The minimum-norm current estimates
• Morphing and averaging
• Creating the BEM meshes
• Miscellaneous C functionality
• Release notes
• Licence agreement
• Setup at the Martinos Center

MNE-MATLAB

• MNE-MATLAB toolbox

Contributing

• Contributing to MNE-Python
• Setting up your local environment for MNE-Python development
• MNE-Python coding conventions