A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG.

Cui, Jian; Lan, Zirui; Liu, Yisi; Li, Ruilin; Li, Fan; Sourina, Olga; Müller-Wittig, Wolfgang

Cui, Jian; Lan, Zirui; Liu, Yisi; Li, Ruilin; Li, Fan; Sourina, Olga; Müller-Wittig, Wolfgang.

Afiliación

Cui J; Fraunhofer Singapore, Nanyang Technological University, Singapore. Electronic address: cuijian@ntu.edu.sg.
Lan Z; Fraunhofer Singapore, Singapore. Electronic address: lan.zirui@fraunhofer.sg.
Liu Y; Fraunhofer Singapore, Singapore. Electronic address: liu.yisi@fraunhofer.sg.
Li R; Nanyang Technological University, Singapore. Electronic address: RUILIN001@e.ntu.edu.sg.
Li F; Fraunhofer Singapore, Nanyang Technological University, Singapore. Electronic address: lifan@ntu.edu.sg.
Sourina O; Fraunhofer Singapore, Nanyang Technological University, Singapore. Electronic address: EOSourina@ntu.edu.sg.
Müller-Wittig W; Fraunhofer Singapore, Nanyang Technological University, Singapore. Electronic address: Wolfgang.Mueller-wittig@fraunhofer.sg.

Methods ; 202: 173-184, 2022 06.

Article en En | MEDLINE | ID: mdl-33901644

Asunto(s)

Electroencefalografía; Vigilia; Artefactos; Humanos; Aprendizaje Automático; Redes Neurales de la Computación

Palabras clave

Class activation mapping; Convolutional neural network; Driver drowsiness detection; Interpretable CNN; Network visualization; Single-channel EEG

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Vigilia / Electroencefalografía Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: Methods Asunto de la revista: BIOQUIMICA Año: 2022 Tipo del documento: Article

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