EEG-Based Subject- and Session-independent Drowsiness Detection: An Unsupervised Approach

Author:

Pal Nikhil R.,Chuang Chien-Yao,Ko Li-Wei,Chao Chih-Feng,Jung Tzyy-Ping,Liang Sheng-Fu,Lin Chin-Teng

Publisher

Springer Science and Business Media LLC

Cited by 67 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. SADNet: sustained attention decoding in a driving task by self-attention convolutional neural network;Brain-Apparatus Communication: A Journal of Bacomics;2024-09-13

2. TFAC-Net: A Temporal-Frequential Attentional Convolutional Network for Driver Drowsiness Recognition With Single-Channel EEG;IEEE Transactions on Intelligent Transportation Systems;2024-07

3. Use of cognitive load measurements to design a new architecture of intelligent learning systems;Expert Systems with Applications;2024-03

4. Identification of Sleepy Drivers as a Means of Maintaining Roadway Safety;2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI);2023-12-29

5. Cross-subject Drowsiness Recognition Model Based on the Multi-scale Parallel Convolution of EEG Channels;2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom);2023-12-21

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