MNEflow: Neural networks for EEG/MEG decoding and interpretation

Ivan Zubarev; Gavriela Vranou; Lauri Parkkonen

doi:10.1016/j.softx.2021.100951

MNEflow: Neural networks for EEG/MEG decoding and interpretation

Ivan Zubarev^*, Gavriela Vranou, Lauri Parkkonen

^*Corresponding author for this work

Department of Neuroscience and Biomedical Engineering

Research output: Contribution to journal › Article › Scientific › peer-review

3 Citations (Scopus)

579 Downloads (Pure)

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.

Original language	English
Article number	100951
Pages (from-to)	1-5
Number of pages	5
Journal	SoftwareX
Volume	17
DOIs	https://doi.org/10.1016/j.softx.2021.100951
Publication status	Published - Jan 2022
MoE publication type	A1 Journal article-refereed

Keywords

Electroencephalography
Machine learning
Magnetoencephalography
Neural networks
Tensorflow

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10.1016/j.softx.2021.100951Licence: CC BY-NC-ND

MNEflow_Neural networks for EEG_MEG decoding and interpretationFinal published version, 1.07 MBLicence: CC BY-NC-ND

Cite this

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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.",

keywords = "Electroencephalography, Machine learning, Magnetoencephalography, Neural networks, Tensorflow",

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AB - 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.

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KW - Machine learning

KW - Magnetoencephalography

KW - Neural networks

KW - Tensorflow

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ER -

MNEflow: Neural networks for EEG/MEG decoding and interpretation

Abstract

Keywords

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Other files and links

HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography

Aalto Neuroimaging Infrastructure

Cite this

MNEflow: Neural networks for EEG/MEG decoding and interpretation

Abstract

Keywords

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Projects

HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography

Equipment

Aalto Neuroimaging Infrastructure

Cite this