Cross-subject Drowsiness Recognition Model Based on the Multi-scale Parallel Convolution of EEG Channels

Cross-subject electroencephalogram (EEG) drowsiness recognition is currently one of the most efficient methods. However, the traditional cross-subject approaches overlook the correlation between channel sub-features and effectively utilize the potential connection between frequency features and drow...

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Vydáno v:	2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom) s. 709 - 714
Hlavní autoři:	Li, Songquan, Zhu, Rongbo, Yuan, Jun, Wan, Shaohua
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	IEEE 21.12.2023
Témata:	Brain modeling Convolution Correlation Cross-subject Deep learning Drowsiness recognition Electric potential Electroencephalography Electroencephalography (EEG) Feature extraction Grouped features
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