Abstract
Accurate estimation of driver’s alert/drowsy state is one of the very essential feature of driver assistance systems. Physiological signals are the direct indication of a driver’s cognitive state. We propose a method using Electroencephalograms (EEG) of the driver to determine the state of consciousness. This paper discusses about EEG analysis using features extracted in both time and frequency domain for alpha and theta frequency bands is used. We have investigated the feature extraction and elimination techniques combined with classification techniques for their performance. Publicly available Sleep data sets from Physionet were used for the proposed study. The signals from Fpz–Oz electrode are divided into smaller segments before estimating the features. Twenty features were used for experimentation. The feature extraction done using ICA showed improved performance over ICA in terms of classification accuracy. The recursive feature elimination technique, when used with the neural network, showed an overall improved performance of 92% accuracy. The proposed method can be used to determine the driver status and further to predict the driver’s alertness
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Nissimagoudar, P.C., Nandi, A.V., Gireesha, H.M. (2021). A Feature Extraction and Selection Method 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. https://doi.org/10.1007/978-3-030-49345-5_31
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DOI: https://doi.org/10.1007/978-3-030-49345-5_31
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