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.
Getting started
MNE basics
More help
Mailing list for analysis talk
GitHub issues for requests and bug reports
Gitter to chat with devs
Getting your data into MNE
Examples
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
Background
Preprocessing your data
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
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
Tutorials
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
Background
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
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
Background
Sensor Space
Examples
Source Space
Decoding
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
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
All realtime functionality has migrated to mne_realtime
.
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