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.

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¶

Parsing events from raw data¶

The Info data structure¶

Working with sensor locations¶

Configuring MNE-Python¶

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 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¶

Working with events¶

Annotating 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¶

Interpolating bad channels¶

Rejecting bad data spans¶

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¶

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

The Epochs data structure: discontinuous data¶

Visualizing epoched data¶

Working with Epoch metadata¶

Exporting Epochs to Pandas DataFrames¶

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¶

Visualizing Evoked data¶

EEG processing and Event Related Potentials (ERPs)¶

Plotting whitened data¶

Time-frequency analysis¶

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

Frequency and time-frequency sensors analysis¶

Source modeling¶

These tutorials cover estimation of cortical activity from sensor recordings.

FreeSurfer MRI reconstruction¶

How MNE uses FreeSurfer’s outputs¶

Source reconstruction using an LCMV beamformer¶

Computing a covariance matrix¶

Source localization with equivalent current dipole (ECD) fit¶

The role of dipole orientations in distributed source localization¶

EEG source localization given electrode locations on an MRI¶

EEG forward operator with a template MRI¶

Editing BEM surfaces in Blender¶

Head model and forward computation¶

Source localization with MNE/dSPM/sLORETA/eLORETA¶

Computing various MNE solutions¶

The SourceEstimate data structure¶

Source alignment and coordinate frames¶

Visualize source time courses (stcs)¶

Statistical analysis of sensor data¶

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

Non-parametric 1 sample cluster statistic on single trial power¶

Visualising statistical significance thresholds on EEG data¶

Non-parametric between conditions cluster statistic 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¶

Mass-univariate twoway repeated measures ANOVA on single trial power¶

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¶

Decoding (MVPA)¶

Simulation¶

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