Adaptive neural network classifier for decoding MEG signals
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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Date
2019-08-15
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Mcode
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Language
en
Pages
10
425-434
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