Adaptive neural network classifier for decoding MEG signals.
Neuroimage
; 197: 425-434, 2019 08 15.
Article
em En
| MEDLINE
| ID: mdl-31059799
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).
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Processamento de Sinais Assistido por Computador
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Encéfalo
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Mapeamento Encefálico
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Magnetoencefalografia
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Redes Neurais de Computação
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Potenciais Evocados
Limite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Neuroimage
Assunto da revista:
DIAGNOSTICO POR IMAGEM
Ano de publicação:
2019
Tipo de documento:
Article