The DOI matches its Crossref record; 191 of the 191 references it cites were found in a registry or an index.
A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems
Open accessAbstract
Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.
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A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Zadeh, L. A. (1965) · Information and Control · doi:10.1016/s0019-9958(65)90241-x · Cited by 67,855
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 48,949
Zadeh, L. A. (1996) · Cited by 46,844
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,564
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
The Structure and Function of Complex Networks
Newman, M. E. J. (2003) · SIAM Review · doi:10.1137/s003614450342480 · Cited by 18,719
Centrality in social networks conceptual clarification
Freeman, L. C. (1978) · Social Networks · doi:10.1016/0378-8733(78)90021-7 · Cited by 17,062
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Cited by
Multimodal emotion recognition: A comprehensive review, trends, and challenges
Ramaswamy, M. P. A., & Palaniswamy, S. (2024) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1563 · Cited by 70
Metadata
- Authors
- Stancin, I., Cifrek, M., & Jovic, A.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2021-05-30
- Volume
- 21
- Issue
- 11
- Pages
- 3786
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W3164743527
- Cited by
- 269
- References
- 191
- FWCI
- 21.35
- Type
- journal article
- Language
- en
- Open access
- gold
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