Gu, Yi and Xia, Kaijian and Lai, Khin-Wee and Jiang, Yizhang and Qian, Pengjiang and Gu, Xiaoqing (2023) Transferable Takagi-Sugeno-Kang Fuzzy Classifier With Multi-Views for EEG-Based Driving Fatigue Recognition in Intelligent Transportation. IEEE Transactions on Intelligent Transportation Systems, 24 (12). pp. 15807-15817. ISSN 1524-9050, DOI https://doi.org/10.1109/TITS.2022.3220597.
Full text not available from this repository.Abstract
The safety monitoring system of intelligent transportation provides driving fatigue warning and risk control. Electroencephalogram (EEG) signals can directly reflect the neuronal activity of the brain. The detection and early warning of driving fatigue using EEG signals has important practical significance. However, because of the non-stationarity and timeliness of EEG signals, the single feature detection method is significantly impacted by data distribution differences. In this paper, in the framework of multi-input multi-output (MIMO) Takagi-Sugeno-Kang (TSK) fuzzy system, transferable TSK fuzzy classifier with multi-views (T-TSK-MV) is developed for EEG-based driving fatigue recognition in intelligent transportation. First, in view-specific consequent parameter learning, the view-specific consequent regularizer is designed based on technologies of ridge regression, maximum mean discrepancy (MMD), and manifold regularization, which becomes the bridge to transfer the discriminative information from the related domain to the target domain. In addition, the $\textbackslashell_{2,1} $ -norm sparse constraint on consequent parameters is used to simplify fuzzy rules. Then multi-view learning is integrated into the consequent parameter learning, in which T-TSK-MV explores the view-shared consequent regularizer and adaptively assigns weights to each view. The $\textbackslashell_{2,1} $ -norm sparse constraint on view-shared consequent regularizer can effectively exploit the local structure of multi-view data. Finally, the fuzzy classifier is constructed on view-specific regularizers and view weights. The experiment on real-word datasets shows that the proposed fuzzy classifier can significantly improve the driving fatigue recognition performance.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) (62172192) ; (62171203) ; (U20A20228), Natural Science Foundation of Jiangsu Province (BK20210449), Fundamental Research Funds for the Central Universities (JUSRP122035), Future Network Scientific Research Fund Project (FNSRFP-2021-YB-36), Science and Technology Project of Changzhou City (CE20215032) |
| Uncontrolled Keywords: | Multi-input multi-output; Takagi-Sugeno-Kang fuzzy classifier; electroencephalogram; driving fatigue recognition |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Biomedical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 03 Jul 2025 07:24 |
| Last Modified: | 03 Jul 2025 07:24 |
| URI: | http://eprints.um.edu.my/id/eprint/51006 |
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