Microstate analysis for use with MNE-Python

A small module that works with MNE-Python to perform microstate analysis in EEG

and MEG data.

To learn more about microstate analysis, read the paper:

Pascual-Marqui, R. D., Michel, C. M., & Lehmann, D. (1995). Segmentation of

brain electrical activity into microstates: model estimation and validation.

IEEE Transactions on Biomedical Engineering.

https://ieeexplore.ieee.org/document/391164

Installation

Install this package using PIP:

pip install mne-microstates

Usage

import mne

import mne_microstates



# Load MNE sample dataset

from mne.datasets import sample

fname = sample.data_path() / 'MEG/sample/sample_audvis_filt.fif'

raw = mne.io.read_raw_fif(fname, preload=True)



# Always use an average EEG reference when doing microstate analysis

raw.set_eeg_reference('average')



# Highpass filter the data a little bit

raw.filter(0.2, None)



# Selecting the sensor types to use in the analysis. In this example, we

# use only EEG channels

raw.pick_types(meg=False, eeg=True)



# Segment the data into 6 microstates

maps, segmentation = mne_microstates.segment(raw.get_data(), n_states=6)



# Plot the topographic maps of the found microstates

mne_microstates.plot_maps(maps, raw.info)



# Plot the segmentation of the first 500 samples

mne_microstates.plot_segmentation(segmentation[:500], raw.get_data()[:, :500], raw.times[:500])

Author

Marijn van Vliet w.m.vanvliet@gmail.com