Multiscale space-time-frequency feature-guided multitask learning CNN for motor imagery EEG classification.

Liu, Xiuling; Lv, Linyang; Shen, Yonglong; Xiong, Peng; Yang, Jianli; Liu, Jing

Liu, Xiuling; Lv, Linyang; Shen, Yonglong; Xiong, Peng; Yang, Jianli; Liu, Jing.

Afiliação

Liu X; College of Electronic Information Engineering, Hebei University, Baoding, Hebei 071000, People's Republic of China.
Lv L; Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Hebei 071000, People's Republic of China.
Shen Y; College of Electronic Information Engineering, Hebei University, Baoding, Hebei 071000, People's Republic of China.
Xiong P; Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Hebei 071000, People's Republic of China.
Yang J; College of Electronic Information Engineering, Hebei University, Baoding, Hebei 071000, People's Republic of China.
Liu J; Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei University, Hebei 071000, People's Republic of China.

J Neural Eng ; 18(2)2021 02 24.

Article em En | MEDLINE | ID: mdl-33395676

Assuntos

Interfaces Cérebro-Computador; Algoritmos; Eletroencefalografia/métodos; Humanos; Imaginação; Redes Neurais de Computação

Palavras-chave

EEG; deep learning; motor imagery; multiscale feature; multitask learning

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Interfaces Cérebro-Computador Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: J Neural Eng Assunto da revista: NEUROLOGIA Ano de publicação: 2021 Tipo de documento: Article País de publicação: Reino Unido

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