User Manual¶

If you are new to MNE, consider first reading the Cookbook, as it gives some simple steps for starting with analysis. The other sections provide more in-depth information about how to use the software. You can also jump to the Python API Reference for specific Python function and class usage information.

Cookbook

A quick run-through of the basic steps involved in M/EEG source analysis.

Cookbook

Reading your data

How to get your raw data loaded in MNE.

Preprocessing

Dealing with artifacts and noise sources in data.

Source localization

Projecting raw data into source (brain) space.

Time-frequency analysis

Decomposing time-domain signals into time-frequency representations.

Spectral and Time-frequency Analysis
- Source Space

Statistics

Using parametric and non-parametric tests with M/EEG data.

Statistics
- Parametric statistics
- Non-paramteric statistics

Decoding

Decoding

Datasets

How to use dataset fetchers for public data

Datasets

Migrating

Migrating from EEGLAB

Pitfalls

Pitfalls
- Float64 vs float32

C Tools

Additional information about various MNE-C tools.

MATLAB Tools

Information about the MATLAB toolbox.

Matlab toolbox

Appendices

More details about our implementations and software.