Examples Gallery

The examples gallery provides working code samples demonstrating various analysis and visualization techniques. These examples often lack the narrative explanations seen in the tutorials, and do not follow any specific order. These examples are a useful way to discover new analysis or plotting ideas, or to see how a particular technique you’ve read about can be applied using MNE-Python.

Warning

These examples sometimes use simulations or shortcuts (such as intentionally adding noise to recordings) to illustrate a point. Use caution when copy-pasting code samples.

Input/Output

Reading and writing files. See also our NumPy’s Documentation on manipulating data structures.

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

Read and visualize projections (SSP and other)

Data Simulation

Tools to generate simulation data.

Generate simulated evoked data

Generate simulated raw data

Simulate raw data using subject anatomy

Generate simulated source data

Preprocessing

Examples related to data preprocessing (artifact detection / rejection etc.)

Define target events based on time lag, plot evoked response

Transform EEG data using current source density (CSD)

Show EOG artifact timing

Find ECG artifacts

Find EOG artifacts

Find MEG reference channel artifacts

Visualise NIRS artifact correction methods

Compare the different ICA algorithms in MNE

Interpolate bad channels for MEG/EEG channels

Querying epochs with rich metadata

Maxwell filter data with movement compensation

Annotate movement artifacts and reestimate dev_head_t

Annotate muscle artifacts

Plot sensor denoising using oversampled temporal projection

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

Looking at data and processing output.

Plotting sensor layouts of EEG systems

How to convert 3D electrode positions to a 2D image.

Visualize channel over epochs as an image

Plotting EEG sensors on the scalp

How to plot topomaps the way EEGLAB does

Plotting topographic arrowmaps of evoked data

Plotting topographic maps of evoked data

Whitening evoked data with a noise covariance

Make an MNE-Report with a Slider

Plotting sensor layouts of MEG systems

Plot the MNE brain and helmet

Plot a cortical parcellation

Make figures more publication ready

Plot single trial activity, grouped by ROI and sorted by RT

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-Frequency Examples

Some examples of how to explore time-frequency content of M/EEG data with MNE.

Compute a cross-spectral density (CSD) matrix

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 source power spectral density (PSD) in a label

Compute source power spectral density (PSD) of VectorView and OPM data

Compute induced power in the source space with dSPM

Temporal whitening with AR model

Compute and visualize ERDS maps

Explore event-related dynamics for specific frequency bands

Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell)

Statistics Examples

Some examples of how to compute statistics on M/EEG data with MNE.

Permutation F-test on sensor data with 1D cluster level

FDR correction on T-test on sensor data

Regression on continuous data (rER[P/F])

Permutation T-test on sensor data

Analysing continuous features with binning and regression in sensor space

Machine Learning (Decoding, Encoding, and MVPA)

Decoding, encoding, and general machine learning examples.

Representational Similarity Analysis

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)

Decoding in time-frequency space using Common Spatial Patterns (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

Receptive Field Estimation and Prediction

Connectivity Analysis Examples

Examples demonstrating connectivity analysis in sensor and source space.

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 envelope correlations in source space

Compute envelope correlations in volume source space

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

Forward modeling

From BEM segmentation, coregistration, setting up source spaces to actual computation of forward solution.

Display sensitivity maps for EEG and MEG sensors

Generate a left cerebellum volume source space

Use source space morphing

Inverse problem and source analysis

Estimate source activations, extract activations in labels, morph data between subjects etc.

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 beamformer

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM

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 sparse inverse solution with mixed norm: MxNE and irMxNE

Compute MNE inverse solution on evoked data in a mixed source space

Compute source power estimate by projecting the covariance with MNE

Morph surface source estimate

Morph volumetric source estimate

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

Compute cross-talk functions for LCMV beamformers

Compute Rap-Music on evoked data

Reading an inverse operator

Reading an STC file

Compute spatial resolution metrics in source space

Compute spatial resolution metrics to compare MEG with EEG+MEG

Estimate data SNR using an inverse

Computing source space SNR

Time-frequency beamforming using DICS

Compute MxNE with time-frequency sparse prior

Plotting the full vector-valued MNE solution

Examples on open datasets

Some demos on common/public datasets using MNE.

Brainstorm raw (median nerve) dataset

HF-SEF dataset

Single trial linear regression analysis with the LIMO dataset

Optically pumped magnetometer (OPM) data

From raw data to dSPM on SPM Faces dataset