Abstract
Driver’s drowsiness becomes a prominent factor that causes the growing number of a road accident in the past few years and turns out to be perturbing for road safety. This research presents approaches for drowsiness and alertness recognition based on the electroencephalography (EEG) and power spectrum to evaluate the driver’s vigilance level in a static driving simulator. The EEG databases are validated using the Karolinska sleepiness scale (KSS) and reaction time (RT). Frequency-domain power spectral density (PSD) feature extraction techniques were evaluated (periodogram, Lomb-Scargle, Thompson multitaper, and Welch) with supervised learning classifiers (MLNN, QSVM, and KNN). The highest accuracy is attained from MLNN using Lomb-Scargle PSD with 96.3% and the minimum accuracy is attained from QSVM and KNN with both 62.2% using periodogram and Welch PSD features set respectively.
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The authors are grateful to the Ministry of Higher Education (MoHE) Malaysia, for providing the funding of this research (Ref: FRGS/1/2016/TK04/UNIKL/01/1).
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Mohamed, F., Krishnan, P., Yaacob, S. (2019). Validation of Driver’s Cognitive Load on Driving Performance Using Spectral Estimation Based on EEG Frequency Spectrum. In: Abu Bakar, M., Mohamad Sidik, M., Öchsner, A. (eds) Progress in Engineering Technology. Advanced Structured Materials, vol 119. Springer, Cham. https://doi.org/10.1007/978-3-030-28505-0_5
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