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- W2973182172 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." @default.
- W2973182172 created "2019-09-19" @default.
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- W2973182172 date "2019-01-01" @default.
- W2973182172 modified "2023-09-26" @default.
- W2973182172 title "Validation of Driver’s Cognitive Load on Driving Performance Using Spectral Estimation Based on EEG Frequency Spectrum" @default.
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- W2973182172 doi "https://doi.org/10.1007/978-3-030-28505-0_5" @default.
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