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.

Reading data for different recording systems

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

Estimating evoked responses

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

Time-frequency analysis

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

Forward models and source spaces

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

Machine learning models of neural activity

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

Clinical applications

These tutorials illustrate clinical uses of MNE-Python.