An end-to-end 3D convolutional neural network for decoding attentive mental state

The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation...

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Vydané v:Neural networks Ročník 144; s. 129 - 137
Hlavní autori: Zhang, Yangsong, Cai, Huan, Nie, Li, Xu, Peng, Zhao, Sirui, Guan, Cuntai
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.12.2021
Predmet:
3D convolutional neural network
Attention
BCI
Deep learning
EEG
Deep learning
BCI
EEG
3D convolutional neural network
Attention
ISSN:0893-6080, 1879-2782, 1879-2782
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Popis
Shrnutí:The detection of attentive mental state plays an essential role in the neurofeedback process and the treatment of Attention Deficit and Hyperactivity Disorder (ADHD). However, the performance of the detection methods is still not satisfactory. One of the challenges is to find a proper representation for the electroencephalogram (EEG) data, which could preserve the temporal information and maintain the spatial topological characteristics. Inspired by the deep learning (DL) methods in the research of brain–computer interface (BCI) field, a 3D representation of EEG signal was introduced into attention detection task, and a 3D convolutional neural network model with cascade and parallel convolution operations was proposed. The model utilized three cascade blocks, each consisting of two parallel 3D convolution branches, to simultaneously extract the multi-scale features. Evaluated on a public dataset containing twenty-six subjects, the proposed model achieved better performance compared with the baseline methods under the intra-subject, inter-subject and subject-adaptive classification scenarios. This study demonstrated the promising potential of the 3D CNN model for detecting attentive mental state.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2021.08.019