# Tutorials#

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.

## Introductory tutorials#

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

Getting started with mne.Report

## Reading data for different recording systems#

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 EEG devices

Importing data from fNIRS devices

Working with CTF data: the Brainstorm auditory dataset

Importing Data from Eyetracking devices

## Working with continuous data#

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

Built-in plotting methods for Raw objects

## Preprocessing#

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

Overview of artifact detection

Rejecting bad data spans and breaks

Background information on filtering

Repairing artifacts with regression

Background on projectors and projections

Extracting and visualizing subject head movement

Signal-space separation (SSS) and Maxwell filtering

Preprocessing functional near-infrared spectroscopy (fNIRS) data

Preprocessing optically pumped magnetometer (OPM) MEG data

Working with eye tracker data in MNE-Python

## Segmenting continuous data into epochs#

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

The Epochs data structure: discontinuous data

Regression-based baseline correction

Auto-generating Epochs metadata

Exporting Epochs to Pandas DataFrames

Divide continuous data into equally-spaced epochs

## Estimating evoked responses#

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

The Evoked data structure: evoked/averaged data

EEG analysis - Event-Related Potentials (ERPs)

## Time-frequency analysis#

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

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#

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

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

## Source localization and inverses#

These tutorials cover estimation of cortical activity from sensor recordings.

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#

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

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#

These tutorials cover within-subject 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#

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

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

## Clinical applications#

These tutorials illustrate some clinical use cases.

### MNE-GUI-addons examples#

The `mne_gui_addons`

package supports some clinical use cases:

Locating intracranial electrode contacts

### MNE-Python examples#

MNE-Python also supports some clinical use cases directly:

Sleep stage classification from polysomnography (PSG) data

## Simulation#

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

Corrupt known signal with point spread