Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 418-425, 2023.
Artigo
em Chinês
| WPRIM
| ID: wpr-981558
ABSTRACT
The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Fatores de Tempo
/
Encéfalo
/
Redes Neurais de Computação
/
Imagens, Psicoterapia
/
Eletroencefalografia
Limite:
Humanos
Idioma:
Chinês
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2023
Tipo de documento:
Artigo
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