Electroencephalogram-Based Multi-Class Driver Fatigue Detection using Power Spectral Density and Lightweight Convolutional Neural Networks

https://doi.org/10.5614/j.eng.technol.sci.2025.57.4.2

Authors

  • Wiwit Suprihatiningsih Department of Mechanical Engineering, Universitas Mercu Buana, Jalan Raya Meruya Selatan 11650, Jakarta
  • Dedik Romahadi Department of Mechanical Engineering, Universitas Mercu Buana, Jalan Raya Meruya Selatan 11650, Jakarta https://orcid.org/0000-0002-9835-5453
  • Aberham Genetu Feleke School of Mechanical Engineering, Beijing Institute of Technology, No. 5 Zhongguancun South Street 100811
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Abstract

Driver fatigue is the primary factor contributing to traffic accidents globally. To address this challenge, the electroencephalogram (EEG) has been proven reliable for assessing sleepiness, fatigue, and performance levels. Although alertness monitoring through EEG analysis has shown progress, its use is affected by complicated methods of collecting data and labelling more than two classes. Based on previous research, the original form of EEG signals or power spectral density (PSD) has been extensively applied to detect driver fatigue. This method needs a large, deep neural network to produce valuable features, requiring significant computational training resources. More observations regarding feature extraction and classification models are needed to reduce computational cost and optimize accuracy values. Therefore, this research aimed to propose a PSD-based feature optimization on a lightweight convolutional neural network (CNN) model. Five types of statistical functions and four types of signal power ratios were applied, and the best features were selected based on ranking algorithms. The results showed that feature optimization using the Relief Feature (ReliefF) algorithm had the highest accuracy. The proposed lightweight CNN model obtained an average intra-subject accuracy of 71.01%, while the cross-subject accuracy was 69.07%.

How to Cite

Wiwit Suprihatiningsih, Dedik Romahadi, Aberham Genetu Feleke (2025). Electroencephalogram-Based Multi-Class Driver Fatigue Detection using Power Spectral Density and Lightweight Convolutional Neural Networks. Journal of Engineering and Technological Sciences, Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August , 445-457. https://doi.org/10.5614/j.eng.technol.sci.2025.57.4.2

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Date Log

Submitted
July 6, 2024
Accepted
May 26, 2025
Published
July 25, 2025

Keywords

  • convolutional neural networks
  • electroencephalogram
  • fatigue detection
  • multi-class classification
  • power spectral density

Issue

Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August

Section

Articles

How to Cite

Electroencephalogram-Based Multi-Class Driver Fatigue Detection using Power Spectral Density and Lightweight Convolutional Neural Networks. (2025). Journal of Engineering and Technological Sciences, 57(4), 445-457. https://doi.org/10.5614/j.eng.technol.sci.2025.57.4.2

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