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  • Install
  • Documentation
  • API Reference
  • Get help
  • Development

Section Navigation

  • Tutorials
    • Introductory tutorials
      • Overview of MEG/EEG analysis with MNE-Python
      • Modifying data in-place
      • Parsing events from raw data
      • The Info data structure
      • Working with sensor locations
      • Configuring MNE-Python
      • Getting started with mne.Report
    • Reading data for different recording systems
      • Importing data from MEG devices
      • Importing data from EEG devices
      • Importing data from fNIRS devices
      • Working with CTF data: the Brainstorm auditory dataset
    • Working with continuous data
      • The Raw data structure: continuous data
      • Working with events
      • Annotating continuous data
      • Built-in plotting methods for Raw objects
    • Preprocessing
      • Overview of artifact detection
      • Handling bad channels
      • Rejecting bad data spans and breaks
      • Background information on filtering
      • Filtering and resampling data
      • Repairing artifacts with regression
      • Repairing artifacts with ICA
      • Background on projectors and projections
      • Repairing artifacts with SSP
      • Setting the EEG reference
      • Extracting and visualizing subject head movement
      • Signal-space separation (SSS) and Maxwell filtering
      • Preprocessing functional near-infrared spectroscopy (fNIRS) data
    • Segmenting continuous data into epochs
      • The Epochs data structure: discontinuous data
      • Regression-based baseline correction
      • Visualizing epoched data
      • Working with Epoch metadata
      • Auto-generating Epochs metadata
      • Exporting Epochs to Pandas DataFrames
      • Divide continuous data into equally-spaced epochs
    • Estimating evoked responses
      • The Evoked data structure: evoked/averaged data
      • Visualizing Evoked data
      • EEG analysis - Event-Related Potentials (ERPs)
      • Plotting whitened data
    • Time-frequency analysis
      • The Spectrum and EpochsSpectrum classes: frequency-domain data
      • Frequency and time-frequency sensor analysis
      • Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset
    • Forward models and source spaces
      • FreeSurfer MRI reconstruction
      • Source alignment and coordinate frames
      • Using an automated approach to coregistration
      • Head model and forward computation
      • EEG forward operator with a template MRI
      • How MNE uses FreeSurfer’s outputs
      • Fixing BEM and head surfaces
      • Computing a covariance matrix
    • Source localization and inverses
      • The SourceEstimate data structure
      • Source localization with equivalent current dipole (ECD) fit
      • Source localization with MNE, dSPM, sLORETA, and eLORETA
      • The role of dipole orientations in distributed source localization
      • Computing various MNE solutions
      • Source reconstruction using an LCMV beamformer
      • Visualize source time courses (stcs)
      • EEG source localization given electrode locations on an MRI
      • Brainstorm Elekta phantom dataset tutorial
      • Brainstorm CTF phantom dataset tutorial
      • 4D Neuroimaging/BTi phantom dataset tutorial
    • Statistical analysis of sensor data
      • Statistical inference
      • Visualising statistical significance thresholds on EEG data
      • Non-parametric 1 sample cluster statistic on single trial power
      • Non-parametric between conditions cluster statistic on single trial power
      • Mass-univariate twoway repeated measures ANOVA on single trial power
      • Spatiotemporal permutation F-test on full sensor data
    • Statistical analysis of source estimates
      • Permutation t-test on source data with spatio-temporal clustering
      • 2 samples permutation test on source data with spatio-temporal clustering
      • Repeated measures ANOVA on source data with spatio-temporal clustering
    • Machine learning models of neural activity
      • Spectro-temporal receptive field (STRF) estimation on continuous data
      • Decoding (MVPA)
    • Clinical applications
      • Locating intracranial electrode contacts
      • Working with sEEG data
      • Working with ECoG data
      • Sleep stage classification from polysomnography (PSG) data
    • Simulation
      • Creating MNE-Python data structures from scratch
      • Corrupt known signal with point spread
      • DICS for power mapping
  • Examples
    • Input/Output
      • Getting averaging info from .fif files
      • How to use data in neural ensemble (NEO) format
      • Reading/Writing a noise covariance matrix
      • Reading XDF EEG data
    • Data Simulation
      • Compare simulated and estimated source activity
      • Generate simulated evoked data
      • Generate simulated raw data
      • Simulate raw data using subject anatomy
      • Generate simulated source data
    • Preprocessing
      • 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)
      • Show EOG artifact timing
      • Reduce EOG artifacts through regression
      • 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
      • Annotate muscle artifacts
      • Removing muscle ICA components
      • Plot sensor denoising using oversampled temporal projection
      • Shifting time-scale in evoked data
      • Remap MEG channel types
      • XDAWN Denoising
    • Visualization
      • How to convert 3D electrode positions to a 2D image
      • Plotting with mne.viz.Brain
      • 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 sensor layouts of MEG systems
      • Plot the MNE brain and helmet
      • Plotting sensor layouts of EEG systems
      • 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
      • 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
      • 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)
      • 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
      • 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
      • Compute Spectro-Spatial Decomposition (SSD) spatial filters
    • Connectivity Analysis Examples
    • Forward modeling
      • Display sensitivity maps for EEG and MEG sensors
      • Generate a left cerebellum volume source space
      • Use source space morphing
    • Inverse problem and source analysis
      • 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 surface source estimate
      • 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
      • 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
      • Compute MxNE with time-frequency sparse prior
      • Plotting the full vector-valued MNE solution
    • Examples on open datasets
      • 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
  • Glossary
  • Implementation details
  • Design philosophy
  • Example datasets
  • Command-line tools
  • Migrating from other analysis software
  • The typical M/EEG workflow
  • How to cite MNE-Python
  • Papers citing MNE-Python

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 averaging info from .fif files

Getting averaging info from .fif files
How to use data in neural ensemble (NEO) format

How to use data in neural ensemble (NEO) format

How to use data in neural ensemble (NEO) format
Reading/Writing a noise covariance matrix

Reading/Writing a noise covariance matrix

Reading/Writing a noise covariance matrix
Reading XDF EEG data

Reading XDF EEG data

Reading XDF EEG data

Data Simulation#

Tools to generate simulation data.

Compare simulated and estimated source activity

Compare simulated and estimated source activity

Compare simulated and estimated source activity
Generate simulated evoked data

Generate simulated evoked data

Generate simulated evoked data
Generate simulated raw data

Generate simulated raw data

Generate simulated raw data
Simulate raw data using subject anatomy

Simulate raw data using subject anatomy

Simulate raw data using subject anatomy
Generate simulated source data

Generate simulated source data

Generate simulated source data

Preprocessing#

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

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals

Cortical Signal Suppression (CSS) for removal of cortical signals
Define target events based on time lag, plot evoked response

Define target events based on time lag, plot evoked response

Define target events based on time lag, plot evoked response
Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel

Identify EEG Electrodes Bridged by too much Gel
Transform EEG data using current source density (CSD)

Transform EEG data using current source density (CSD)

Transform EEG data using current source density (CSD)
Show EOG artifact timing

Show EOG artifact timing

Show EOG artifact timing
Reduce EOG artifacts through regression

Reduce EOG artifacts through regression

Reduce EOG artifacts through regression
Find MEG reference channel artifacts

Find MEG reference channel artifacts

Find MEG reference channel artifacts
Visualise NIRS artifact correction methods

Visualise NIRS artifact correction methods

Visualise NIRS artifact correction methods
Compare the different ICA algorithms in MNE

Compare the different ICA algorithms in MNE

Compare the different ICA algorithms in MNE
Interpolate bad channels for MEG/EEG channels

Interpolate bad channels for MEG/EEG channels

Interpolate bad channels for MEG/EEG channels
Maxwell filter data with movement compensation

Maxwell filter data with movement compensation

Maxwell filter data with movement compensation
Annotate movement artifacts and reestimate dev_head_t

Annotate movement artifacts and reestimate dev_head_t

Annotate movement artifacts and reestimate dev_head_t
Annotate muscle artifacts

Annotate muscle artifacts

Annotate muscle artifacts
Removing muscle ICA components

Removing muscle ICA components

Removing muscle ICA components
Plot sensor denoising using oversampled temporal projection

Plot sensor denoising using oversampled temporal projection

Plot sensor denoising using oversampled temporal projection
Shifting time-scale in evoked data

Shifting time-scale in evoked data

Shifting time-scale in evoked data
Remap MEG channel types

Remap MEG channel types

Remap MEG channel types
XDAWN Denoising

XDAWN Denoising

XDAWN Denoising

Visualization#

Looking at data and processing output.

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

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

How to convert 3D electrode positions to a 2D image
Plotting with ``mne.viz.Brain``

Plotting with mne.viz.Brain

Plotting with ``mne.viz.Brain``
Visualize channel over epochs as an image

Visualize channel over epochs as an image

Visualize channel over epochs as an image
Plotting EEG sensors on the scalp

Plotting EEG sensors on the scalp

Plotting EEG sensors on the scalp
Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data

Plotting topographic arrowmaps of evoked data
Plotting topographic maps of evoked data

Plotting topographic maps of evoked data

Plotting topographic maps of evoked data
Whitening evoked data with a noise covariance

Whitening evoked data with a noise covariance

Whitening evoked data with a noise covariance
Plotting sensor layouts of MEG systems

Plotting sensor layouts of MEG systems

Plotting sensor layouts of MEG systems
Plot the MNE brain and helmet

Plot the MNE brain and helmet

Plot the MNE brain and helmet
Plotting sensor layouts of EEG systems

Plotting sensor layouts of EEG systems

Plotting sensor layouts of EEG systems
Plot a cortical parcellation

Plot a cortical parcellation

Plot a cortical parcellation
Make figures more publication ready

Make figures more publication ready

Make figures more publication ready
Plot single trial activity, grouped by ROI and sorted by RT

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

Plot single trial activity, grouped by ROI and sorted by RT
Show noise levels from empty room data

Show noise levels from empty room data

Show noise levels from empty room data
Sensitivity map of SSP projections

Sensitivity map of SSP projections

Sensitivity map of SSP projections
Compare evoked responses for different conditions

Compare evoked responses for different conditions

Compare evoked responses for different conditions
Plot custom topographies for MEG sensors

Plot custom topographies for MEG sensors

Plot custom topographies for MEG sensors
Cross-hemisphere comparison

Cross-hemisphere comparison

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 a cross-spectral density (CSD) matrix

Compute a cross-spectral density (CSD) matrix
Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs

Compute Power Spectral Density of inverse solution from single epochs
Compute power and phase lock in label of the source space

Compute power and phase lock in label of the source space

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) in a label

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

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

Compute source power spectral density (PSD) of VectorView and OPM data
Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM

Compute induced power in the source space with dSPM
Temporal whitening with AR model

Temporal whitening with AR model

Temporal whitening with AR model
Compute and visualize ERDS maps

Compute and visualize ERDS maps

Compute and visualize ERDS maps
Explore event-related dynamics for specific frequency bands

Explore event-related dynamics for specific frequency bands

Explore event-related dynamics for specific frequency bands
Time-frequency on simulated data (Multitaper vs. Morlet vs. Stockwell vs. Hilbert)

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

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

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

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

FDR correction on T-test on sensor data

FDR correction on T-test on sensor data
Regression on continuous data (rER[P/F])

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

Regression on continuous data (rER[P/F])
Permutation T-test on sensor data

Permutation T-test on sensor data

Permutation T-test on sensor data
Analysing continuous features with binning and regression in sensor space

Analysing continuous features with binning and regression in sensor space

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)

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

Motor imagery decoding from EEG data using the Common Spatial Pattern (CSP)
Decoding in time-frequency space using Common Spatial Patterns (CSP)

Decoding in time-frequency space using Common Spatial Patterns (CSP)

Decoding in time-frequency space using Common Spatial Patterns (CSP)
Representational Similarity Analysis

Representational Similarity Analysis

Representational Similarity Analysis
Decoding source space data

Decoding source space data

Decoding source space data
Continuous Target Decoding with SPoC

Continuous Target Decoding with SPoC

Continuous Target Decoding with SPoC
Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions

Decoding sensor space data with generalization across time and conditions
Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques

Analysis of evoked response using ICA and PCA reduction techniques
XDAWN Decoding From EEG data

XDAWN Decoding From EEG data

XDAWN Decoding From EEG data
Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)

Compute effect-matched-spatial filtering (EMS)
Linear classifier on sensor data with plot patterns and filters

Linear classifier on sensor data with plot patterns and filters

Linear classifier on sensor data with plot patterns and filters
Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction

Receptive Field Estimation and Prediction
Compute Spectro-Spatial Decomposition (SSD) spatial filters

Compute Spectro-Spatial Decomposition (SSD) spatial filters

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

Display sensitivity maps for EEG and MEG sensors

Display sensitivity maps for EEG and MEG sensors
Generate a left cerebellum volume source space

Generate a left cerebellum volume source space

Generate a left cerebellum volume source space
Use source space morphing

Use source space morphing

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 MNE-dSPM inverse solution on single epochs

Compute MNE-dSPM inverse solution on single epochs
Compute sLORETA inverse solution on raw data

Compute sLORETA inverse solution on raw data

Compute sLORETA inverse solution on raw data
Compute MNE-dSPM inverse solution on evoked data in volume source space

Compute MNE-dSPM inverse solution on evoked data in volume source space

Compute MNE-dSPM inverse solution on evoked data in volume source space
Source localization with a custom inverse solver

Source localization with a custom inverse solver

Source localization with a custom inverse solver
Compute source level time-frequency timecourses using a DICS beamformer

Compute source level time-frequency timecourses using a DICS beamformer

Compute source level time-frequency timecourses using a DICS beamformer
Compute source power using DICS beamformer

Compute source power using DICS beamformer

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

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

Compute evoked ERS source power using DICS, LCMV beamformer, and dSPM
Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method

Compute a sparse inverse solution using the Gamma-MAP empirical Bayesian method
Extracting time course from source_estimate object

Extracting time course from source_estimate object

Extracting time course from source_estimate object
Generate a functional label from source estimates

Generate a functional label from source estimates

Generate a functional label from source estimates
Extracting the time series of activations in a label

Extracting the time series of activations in a label

Extracting the time series of activations in a label
Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE

Compute sparse inverse solution with mixed norm: MxNE and irMxNE
Compute MNE inverse solution on evoked data with a mixed source space

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

Compute MNE inverse solution on evoked data with a mixed source space
Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE

Compute source power estimate by projecting the covariance with MNE
Morph surface source estimate

Morph surface source estimate

Morph surface source estimate
Morph volumetric source estimate

Morph volumetric source estimate

Morph volumetric source estimate
Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model

Computing source timecourses with an XFit-like multi-dipole model
Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary

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

Compute iterative reweighted TF-MxNE with multiscale time-frequency dictionary
Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph

Visualize source leakage among labels using a circular graph
Plot point-spread functions (PSFs) and cross-talk functions (CTFs)

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

Plot point-spread functions (PSFs) and cross-talk functions (CTFs)
Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers

Compute cross-talk functions for LCMV beamformers
Plot point-spread functions (PSFs) for a volume

Plot point-spread functions (PSFs) for a volume

Plot point-spread functions (PSFs) for a volume
Compute Rap-Music on evoked data

Compute Rap-Music on evoked data

Compute Rap-Music on evoked data
Reading an inverse operator

Reading an inverse operator

Reading an inverse operator
Reading an STC file

Reading an STC file

Reading an STC file
Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space

Compute spatial resolution metrics in source space
Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG

Compute spatial resolution metrics to compare MEG with EEG+MEG
Estimate data SNR using an inverse

Estimate data SNR using an inverse

Estimate data SNR using an inverse
Computing source space SNR

Computing source space SNR

Computing source space SNR
Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior

Compute MxNE with time-frequency sparse prior
Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution

Plotting the full vector-valued MNE solution

Examples on open datasets#

Some demos on common/public datasets using MNE.

Brainstorm raw (median nerve) dataset

Brainstorm raw (median nerve) dataset

Brainstorm raw (median nerve) dataset
HF-SEF dataset

HF-SEF dataset

HF-SEF dataset
Single trial linear regression analysis with the LIMO dataset

Single trial linear regression analysis with the LIMO dataset

Single trial linear regression analysis with the LIMO dataset
Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data

Optically pumped magnetometer (OPM) data
From raw data to dSPM on SPM Faces dataset

From raw data to dSPM on SPM Faces dataset

From raw data to dSPM on SPM Faces dataset

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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Input/Output

On this page
  • Input/Output
  • Data Simulation
  • Preprocessing
  • Visualization
  • Time-Frequency Examples
  • Statistics Examples
  • Machine Learning (Decoding, Encoding, and MVPA)
  • Connectivity Analysis Examples
  • Forward modeling
  • Inverse problem and source analysis
  • Examples on open datasets

© Copyright 2012–2023, MNE Developers. Last updated 2023-02-23 23:06 UTC