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**

- Cite MNE
- Mailing list for analysis talk
- GitHub issues for requests and bug reports
- Gitter to chat with devs

**Getting your data into MNE**

**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
- Brainstorm tutorial datasets
- Optically pumped magnetometer (OPM) data
- MEGSIM experimental and simulation datasets
- MEGSIM single trial simulation dataset
- From raw data to dSPM on SPM Faces dataset

**Background**

**Preprocessing your data**

- 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

- 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**

- 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
- 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
- FreeSurfer integration with MNE-Python

- 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**

**Source Space**

**Decoding**

- 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 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

**Examples**