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.
Note
If example-scripts contain plots and are run locally, using the
interactive interactive flag with python -i tutorial_script.py
keeps them open.
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#
Recipes for reading and writing files. See also our tutorials on reading data from various recording systems and our tutorial on manipulating MNE-Python data structures.
Getting averaging info from .fif files
Getting impedances from raw files
How to use data in neural ensemble (NEO) format
Reading/Writing a noise covariance matrix
Data Simulation#
Tools to generate simulation data.
Compare simulated and estimated source activity
Generate simulated evoked data
Simulate raw data using subject anatomy
Generate simulated source data
Preprocessing#
Examples related to data preprocessing (artifact detection / rejection etc.)
Locating micro-scale intracranial electrode contacts
Using contralateral referencing for EEG
Cortical Signal Suppression (CSS) for removal of cortical signals
Define target events based on time lag, plot evoked response
Identify EEG Electrodes Bridged by too much Gel
Transform EEG data using current source density (CSD)
Reduce EOG artifacts through regression
Automated epochs metadata generation with variable time windows
Find MEG reference channel artifacts
Visualise NIRS artifact correction methods
Compare the different ICA algorithms in MNE
Interpolate bad channels for MEG/EEG channels
Maxwell filter data with movement compensation
Annotate movement artifacts and reestimate dev_head_t
Removing muscle ICA components
Plot sensor denoising using oversampled temporal projection
Shifting time-scale in evoked data
Visualization#
Looking at data and processing output.
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 arrowmaps of evoked data
Plotting topographic maps of evoked data
Whitening evoked data with a noise covariance
Plotting eye-tracking heatmaps in MNE-Python
Plotting sensor layouts of MEG systems
Plotting sensor layouts of EEG systems
Plot single trial activity, grouped by ROI and sorted by RT
Sensitivity map of SSP projections
Compare evoked responses for different conditions
Plot custom topographies for MEG sensors
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 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 vs. Hilbert)
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.
Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Decoding in time-frequency space using Common Spatial Patterns (CSP)
Representational Similarity Analysis
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
Compute effect-matched-spatial filtering (EMS)
Linear classifier on sensor data with plot patterns and filters
Receptive Field Estimation and Prediction
Compute Spectro-Spatial Decomposition (SSD) spatial filters
Connectivity Analysis Examples#
Examples demonstrating connectivity analysis in sensor and source space.
Note
Connectivity functionality has moved into the
mne_connectivity
package. Examples can be found at
Examples.
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
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
Source localization with a custom inverse solver
Compute source level time-frequency timecourses using a DICS beamformer
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 with a mixed source space
Compute source power estimate by projecting the covariance with MNE
Morph volumetric source estimate
Computing source timecourses with an XFit-like multi-dipole model
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers
Plot point-spread functions (PSFs) for a volume
Compute Rap-Music on evoked data
Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG
Estimate data SNR using an inverse
Compute MxNE with time-frequency sparse prior
Compute Trap-Music on evoked data
Plotting the full vector-valued MNE solution
Examples on open datasets#
Some demos on common/public datasets using MNE.
Brainstorm raw (median nerve) dataset
Single trial linear regression analysis with the LIMO dataset
Optically pumped magnetometer (OPM) data
From raw data to dSPM on SPM Faces dataset