MNEflow: Neural networks for EEG/MEG decoding and interpretation

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openAccess

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Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Authors

Date

2022-01

Department

Department of Neuroscience and Biomedical Engineering

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Mcode

Degree programme

Language

en

Pages

5
1-5

Series

SoftwareX, Volume 17

Abstract

MNEflow is a Python package for applying deep neural networks to multichannel electroencephalograpic (EEG) and magnetoencephalographic (MEG) measurements. This software comprises Tensorflow-based implementations of several popular convolutional neural network (CNN) models for EEG–MEG data and introduces a flexible pipeline enabling easy application of the most common preprocessing, validation, and model interpretation approaches. The software aims to save time and computational resources required for applying neural networks to the analysis of EEG and MEG data.

Description

| openaire: EC/H2020/678578/EU//HRMEG

Keywords

Electroencephalography, Machine learning, Magnetoencephalography, Neural networks, Tensorflow

Other note

DOI

10.1016/j.softx.2021.100951

Citation

Zubarev, I, Vranou, G & Parkkonen, L 2022, ' MNEflow: Neural networks for EEG/MEG decoding and interpretation ', SoftwareX, vol. 17, 100951, pp. 1-5 . https://doi.org/10.1016/j.softx.2021.100951

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https://urn.fi/URN:NBN:fi:aalto-202201121156

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[article-cris] Perustieteiden korkeakoulu / SCI