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

^*Tämän työn vastaava kirjoittaja

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Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

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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.

Alkuperäiskieli	Englanti
Artikkeli	100951
Sivut	1-5
Sivumäärä	5
Julkaisu	SoftwareX
Vuosikerta	17
DOI - pysyväislinkit	https://doi.org/10.1016/j.softx.2021.100951
Tila	Julkaistu - tammik. 2022
OKM-julkaisutyyppi	A1 Julkaistu artikkeli, soviteltu

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

MNEflow_Neural networks for EEG_MEG decoding and interpretationFinal published version, 1,07 MBLisenssi: CC BY-NC-ND

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HRMEG: High-resolution magnetoencephalography: Towards non-invasive corticography
Iivanainen, J., Parkkonen, L., Zubarev, I., Anelli, M., Avendano Diaz, J., Pfeiffer, C., Helle, L., Nurminen, M., Zetter, R., Hietala, P., Terborg, H., Yamin, A., Grön, M., Luostarinen, E., Zhigalov, A., Henttonen, M. & Puthanmadam Subramaniyam, N.
22/08/2016 → 31/08/2022
Projekti: EU: ERC grants

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@article{388a2cc824b842b9845f09ac8f1de34b,

title = "MNEflow: Neural networks for EEG/MEG decoding and interpretation",

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",

author = "Ivan Zubarev and Gavriela Vranou and Lauri Parkkonen",

note = "| openaire: EC/H2020/678578/EU//HRMEG ",

year = "2022",

month = jan,

doi = "10.1016/j.softx.2021.100951",

language = "English",

volume = "17",

pages = "1--5",

journal = "SoftwareX",

issn = "2352-7110",

publisher = "Elsevier",

}

TY - JOUR

T1 - MNEflow: Neural networks for EEG/MEG decoding and interpretation

AU - Zubarev, Ivan

AU - Vranou, Gavriela

AU - Parkkonen, Lauri

N1 - | openaire: EC/H2020/678578/EU//HRMEG

PY - 2022/1

Y1 - 2022/1

N2 - 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.

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.

KW - Electroencephalography

KW - Machine learning

KW - Magnetoencephalography

KW - Neural networks

KW - Tensorflow

UR - http://www.scopus.com/inward/record.url?scp=85121965776&partnerID=8YFLogxK

U2 - 10.1016/j.softx.2021.100951

DO - 10.1016/j.softx.2021.100951

M3 - Article

AN - SCOPUS:85121965776

VL - 17

SP - 1

EP - 5

JO - SoftwareX

JF - SoftwareX

SN - 2352-7110

M1 - 100951

ER -

MNEflow: Neural networks for EEG/MEG decoding and interpretation

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

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