These tutorials provide narrative explanations, sample code, and expected output for the most common MNE-Python analysis tasks. The emphasis here is on thorough explanations that get you up to speed quickly, at the expense of covering only a limited number of topics. The sections and tutorials are arranged in a fixed order, so in theory a new user should be able to progress through in order without encountering any cases where background knowledge is assumed and unexplained. More experienced users (i.e., those with significant experience analyzing EEG/MEG signals with different software) can probably skip around to just the topics they need without too much trouble.

Note

If tutorial-scripts contain plots and are run locally, using the
interactive flag with `python -i tutorial_script.py`

keeps them open.

These tutorials cover the basic EEG/MEG pipeline for event-related analysis,
introduce the `mne.Info`

, events, and `mne.Annotations`

data structures, discuss how sensor locations are handled, and introduce some
of the configuration options available.

Overview of MEG/EEG analysis with MNE-Python

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

Getting started with mne.Report

These tutorials cover the basics of loading EEG/MEG data into MNE-Python for various recording devices.

Importing data from MEG devices

Importing data from MEG devices

Importing data from EEG devices

Importing data from EEG devices

Importing data from fNIRS devices

Importing data from fNIRS devices

Working with CTF data: the Brainstorm auditory dataset

Working with CTF data: the Brainstorm auditory dataset

These tutorials cover the basics of loading EEG/MEG data into MNE-Python, and
how to query, manipulate, annotate, plot, and export continuous data in the
`Raw`

format.

The Raw data structure: continuous data

The Raw data structure: continuous data

Working with events

Annotating continuous data

Built-in plotting methods for Raw objects

Built-in plotting methods for Raw objects

These tutorials cover various preprocessing techniques for continuous data, as well as some diagnostic plotting methods.

Overview of artifact detection

Overview of artifact detection

Handling bad channels

Rejecting bad data spans and breaks

Rejecting bad data spans and breaks

Background information on filtering

Background information on filtering

Filtering and resampling data

Repairing artifacts with regression

Repairing artifacts with regression

Repairing artifacts with ICA

Background on projectors and projections

Background on projectors and projections

Repairing artifacts with SSP

Setting the EEG reference

Extracting and visualizing subject head movement

Extracting and visualizing subject head movement

Signal-space separation (SSS) and Maxwell filtering

Signal-space separation (SSS) and Maxwell filtering

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing functional near-infrared spectroscopy (fNIRS) data

These tutorials cover epoched data, and how it differs from working with continuous data.

The Epochs data structure: discontinuous data

The Epochs data structure: discontinuous data

Regression-based baseline correction

Regression-based baseline correction

Visualizing epoched data

Working with Epoch metadata

Auto-generating Epochs metadata

Auto-generating Epochs metadata

Exporting Epochs to Pandas DataFrames

Exporting Epochs to Pandas DataFrames

Divide continuous data into equally-spaced epochs

Divide continuous data into equally-spaced epochs

These tutorials cover estimates of evoked responses (i.e., averages across several repetitions of an experimental condition).

The Evoked data structure: evoked/averaged data

The Evoked data structure: evoked/averaged data

Visualizing Evoked data

EEG analysis - Event-Related Potentials (ERPs)

EEG analysis - Event-Related Potentials (ERPs)

Plotting whitened data

These tutorials cover frequency and time-frequency analysis of neural signals.

The Spectrum and EpochsSpectrum classes: frequency-domain data

The Spectrum and EpochsSpectrum classes: frequency-domain data

Frequency and time-frequency sensor analysis

Frequency and time-frequency sensor analysis

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset

Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset

These tutorials cover how the cortical source locations (source spaces) and forward models (AKA leadfield matrices) are defined.

FreeSurfer MRI reconstruction

Source alignment and coordinate frames

Source alignment and coordinate frames

Using an automated approach to coregistration

Using an automated approach to coregistration

Head model and forward computation

Head model and forward computation

EEG forward operator with a template MRI

EEG forward operator with a template MRI

How MNE uses FreeSurfer’s outputs

How MNE uses FreeSurfer's outputs

Fixing BEM and head surfaces

Computing a covariance matrix

These tutorials cover estimation of cortical activity from sensor recordings.

The SourceEstimate data structure

The SourceEstimate data structure

Source localization with equivalent current dipole (ECD) fit

Source localization with equivalent current dipole (ECD) fit

Source localization with MNE, dSPM, sLORETA, and eLORETA

Source localization with MNE, dSPM, sLORETA, and eLORETA

The role of dipole orientations in distributed source localization

The role of dipole orientations in distributed source localization

Computing various MNE solutions

Computing various MNE solutions

Source reconstruction using an LCMV beamformer

Source reconstruction using an LCMV beamformer

Visualize source time courses (stcs)

Visualize source time courses (stcs)

EEG source localization given electrode locations on an MRI

EEG source localization given electrode locations on an MRI

Brainstorm Elekta phantom dataset tutorial

Brainstorm Elekta phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

Brainstorm CTF phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

4D Neuroimaging/BTi phantom dataset tutorial

These tutorials describe some approaches to statistical analysis of sensor-level data.

Statistical inference

Visualising statistical significance thresholds on EEG data

Visualising statistical significance thresholds on EEG data

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric 1 sample cluster statistic on single trial power

Non-parametric between conditions 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

Mass-univariate twoway repeated measures ANOVA on single trial power

Spatiotemporal permutation F-test on full sensor data

Spatiotemporal permutation F-test on full sensor data

These tutorials cover within-subject statistical analysis of source estimates.

Permutation t-test on source data with spatio-temporal clustering

Permutation t-test on source data with spatio-temporal clustering

2 samples permutation 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

Repeated measures ANOVA on source data with spatio-temporal clustering

These tutorials cover some of the machine learning methods available in MNE-Python.

Spectro-temporal receptive field (STRF) estimation on continuous data

Spectro-temporal receptive field (STRF) estimation on continuous data

Decoding (MVPA)

These tutorials illustrate clinical uses of MNE-Python.

Locating intracranial electrode contacts

Locating intracranial electrode contacts

Working with sEEG data

Working with ECoG data

Sleep stage classification from polysomnography (PSG) data

Sleep stage classification from polysomnography (PSG) data

These tutorials describe how to populate MNE-Python data structures with arbitrary data, using the array-based constructors and the simulation submodule.

Creating MNE-Python data structures from scratch

Creating MNE-Python data structures from scratch

Corrupt known signal with point spread

Corrupt known signal with point spread

DICS for power mapping