Event-related potentials emotion features of sleep-deprived and non-sleep-deprived drivers: a preliminary study

This paper explains the study of Event-Related Potentials (ERP) emotion features related to sleep-deprived and non-sleep-deprived drivers. The study's research protocol consists of four stages - initial brainwave recording, four one-minute emotional stimulation experiments for happy, fear, sad, and calm, three sets of driving tests, and baseline brainwave recording. The data collected is pre-processed and then classified using Binary Linear Regression (BLR). The BLR classification of emotion-based ERP for sleep-deprived and non-sleep-deprived signals shows 86.1% accuracy. Though this result is still at the preliminary stage, it concluded that emotion-based ERPs can be used as promising features of brain signals to detect and classify the sleep-deprived and non-sleep-deprived.

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Publication Details

Journal
Journal of Advanced Research in Applied Sciences and Engineering Technology
Published
2024-10-09
DOI
https://doi.org/10.37934/araset.60.2.207218
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
0.00

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article

Event-related potentials emotion features of sleep-deprived and non-sleep-deprived drivers: a preliminary study

Ali A. Alwan, Abdul Wahab, Nurhafizah Mahri, Muhammad Afiq Ammar Kamaruzzaman et al.
Journal of Advanced Research in Applied Sciences and Engineering Technology
Sleep and Work-Related Fatigue
article

Event-related potentials emotion features of sleep-deprived and non-sleep-deprived drivers: a preliminary study

Ali A. Alwan, Abdul Wahab, Nurhafizah Mahri, Muhammad Afiq Ammar Kamaruzzaman, Marini Othman, Raini Hassan
article en

Abstract

This paper explains the study of Event-Related Potentials (ERP) emotion features related to sleep-deprived and non-sleep-deprived drivers. The study's research protocol consists of four stages - initial brainwave recording, four one-minute emotional stimulation experiments for happy, fear, sad, and calm, three sets of driving tests, and baseline brainwave recording. The data collected is pre-processed and then classified using Binary Linear Regression (BLR). The BLR classification of emotion-based ERP for sleep-deprived and non-sleep-deprived signals shows 86.1% accuracy. Though this result is still at the preliminary stage, it concluded that emotion-based ERPs can be used as promising features of brain signals to detect and classify the sleep-deprived and non-sleep-deprived.

Journal of Advanced Research in Applied Sciences and Engineering Technology
Ramapo College (US), International Islamic University Malaysia (MY)
Korea Institute of Construction Technology, International Islamic University Malaysia
Openalex Percentile: Top 76%
Sleep and Work-Related Fatigue
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Event-related potentials emotion features of sleep-deprived and non-sleep-deprived drivers: a preliminary study — Ali A. Alwan, Abdul Wahab, et al. · Journal of Advanced Research in Applied Sciences and Engineering Technology (2024) | TGRS Research Map | TGRS