EEG-Based Driver Performance Estimation Using Deep Learning
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-6568-7_35
Reference13 articles.
1. Nissimagoudar, P.C., Nandi, A.V., Gireesha, H.M. (2021). Deep Convolution Neural Network-Based Feature Learning Model for EEG Based Driver Alert/Drowsy State Detection. In: Abraham, A., Jabbar, M., Tiwari, S., Jesus, I. (eds) Proceedings of the 11th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2019). SoCPaR 2019. Advances in Intelligent Systems and Computing, vol 1182. Springer, Cham.
2. Hajinoroozi, Mehdi & Mao, Zijing & Huang, Yufei. (2015).“ Prediction of driver's drowsy and alert states from EEG signals with deep learning.“ https://doi.org/10.1109/CAMSAP.2015.7383844.
3. Reddy, Tharun & Behera, Laxmidhar. (2016).“ Online Eye state recognition from EEG data using Deep architectures.“ 000712–000717. https://doi.org/10.1109/SMC.2016.7844325, pp.68--73.
4. Supratak A, Dong H, Wu C, Guo Y (2017) DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG. IEEE Trans Neural Syst Rehabil Eng 25(11):1998–2008
5. Craik, Alexander et al. “Deep learning for electroencephalogram (EEG) classification tasks: a review.” Journal of neural engineering 16 3 (2019)
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