Mining the Potential Temporal Features Based on Wearable EEG Signals for Driving State Analysis
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0669-3_9
Reference12 articles.
1. Qin, X., Yang, P., Shen, Y., Li, M., Hu, J., Yun, J.: Classification of driving fatigue based on EEG signals. In: 2020 International Symposium on Computer, Consumer and Control (IS3C 2020), pp. 508–512. IEEE; IEEE Power Electronics Society; Institute of Science and Technology; Intelligent Living Technology Associates, Taiwan (2021)
2. Wang, F., Wu, S., Ping, J., Xu, Z., Chu, H.: EEG driving fatigue detection with PDC-based brain functional network. IEEE Sens. J. 21(9), 10811–10823 (2021)
3. Qin, Y., et al.: Directed brain network analysis for fatigue driving based on EEG source signals. Entropy 24(8), 1093 (2022)
4. Chen, C., Ji, Z., Sun, Y., Bezerianos, A., Thakor, N., Wang, H.: Self-attentive channel-connectivity capsule network for EEG-based driving fatigue detection. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 3152–3162 (2023)
5. Wang, J., et al.: Driving fatigue detection with three non-hair-bearing EEG channels and modified transformer model. Entropy 24(12), 1715 (2022)
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