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Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection

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Abstract

Drowsiness detection is essential in some critical tasks such as vehicle driving, crane operating, mining blasting, and so on, which can help minimize the risks of inattentiveness. Electroencephalography (EEG) based drowsiness detection methods have been shown to be effective. However, due to the non-stationary nature of EEG signals, techniques such as signal transformation and sub-band extraction are increasingly being used to automatically classify awake and drowsy states. Most of these procedures require high computation time. In this paper, analytical and single-feature computation are used to propose a single-channel EEG-based drowsiness detection method to overcome this. Physionet sleep dataset and the simulated virtual driving dataset were used to test the proposed model. When compared to existing work, the proposed approach yields better results.

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Acknowledgements

We would like to thank Prof. Bao-Liang Lu and his research team for the valuable data arrangement. We appreciate his support in providing us the simulated virtual driving dataset for our current research.

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  1. Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India

    Venkata Phanikrishna Balam & Suchismitha Chinara

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  1. Venkata Phanikrishna Balam
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  2. Suchismitha Chinara
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Correspondence to Venkata Phanikrishna Balam.

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Balam, V.P., Chinara, S. Development of single-channel electroencephalography signal analysis model for real-time drowsiness detection. Phys Eng Sci Med 44, 713–726 (2021). https://doi.org/10.1007/s13246-021-01020-3

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