Adaptive neural network classifier for decoding MEG signals

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openAccess

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Journal Title

Journal ISSN

Volume Title

A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2019-08-15

Major/Subject

Mcode

Degree programme

Language

en

Pages

10
425-434

Series

NeuroImage, Volume 197

Abstract

We introduce two Convolutional Neural Network (CNN )classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, these models can successfully generalize to new subjects in real-time classification enabling more efficient brain–computer interfaces (BCI).

Description

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

Keywords

Brain–computer interface, Convolutional neural network, Magnetoencephalography

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Citation

Zubarev, I, Zetter, R, Halme, H L & Parkkonen, L 2019, ' Adaptive neural network classifier for decoding MEG signals ', NeuroImage, vol. 197, pp. 425-434 . https://doi.org/10.1016/j.neuroimage.2019.04.068