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

Wiwit Suprihatiningsih; Dedik Romahadi; Aberham Genetu Feleke

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

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

Published: 2025-01-01 | DOI: 10.5614/j.eng.technol.sci.2025.57.4.2 | Vol 57, Issue 4 (2025)

Authors

Wiwit Suprihatiningsih

Department of Mechanical Engineering, Universitas Mercu Buana, Jalan Raya Meruya Selatan 11650, Jakarta

ORCID: 0000-0002-4821-3713 · OpenAlex: A5047285800

Dedik Romahadi

Department of Mechanical Engineering, Universitas Mercu Buana, Jalan Raya Meruya Selatan 11650, Jakarta

ORCID: 0000-0002-9835-5453 · OpenAlex: A5081391873

Aberham Genetu Feleke

School of Mechanical Engineering, Beijing Institute of Technology, No. 5 Zhongguancun South Street 100811

ORCID: 0000-0001-9871-5431 · OpenAlex: A5070095781

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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%.

Keywords

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

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Research Intelligence

Data from OpenAlex ↗

Metrics

0.00

FWCIfield-weighted

14th

Percentilevs same year + field

Article

Work type

GOLD

Open Access

Topics

EEG and Brain-Computer Interfaces Primary

0.98

Cognitive Neuroscience › Neuroscience › Life Sciences

Sleep and Work-Related Fatigue

0.98

Elevator Systems and Control

0.94

Related Research

Semantic Profile AI-classified research signals

Core Domains

Convolutional neural network 0.84

level 2

Class (philosophy) 0.65

level 2

Spectral density 0.54

level 2

Secondary Topics

Computer science 0.53 Power (physics) 0.45 Pattern recognition (psychology) 0.45 Automotive engineering 0.40 Artificial intelligence 0.39 Environmental science 0.32 Real-time computing 0.32 Engineering 0.30

Institution Network

Mercu Buana University ID
Wiwit Suprihatiningsih · Dedik Romahadi
Beijing Institute of Technology CN
Aberham Genetu Feleke

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[ref-1] Alasmari, N., Alohali, M. A., Khalid, M., Almalki, N., Motwakel, A., Alsaid, M. I., Osman, A. E., & Alneil, A. A. (2023). Improved metaheuristics with deep learning based object detector for intelligent control in autonomous vehicles. Computers and Electrical Engineering, 108, 108718. https://doi.org/10.1016/J.COMPELECENG.2023.108718 DOI: 10.1016/j.compeleceng.2023.108718

[ref-2] Al-Qazzaz, N. K., Sabir, M. K., Md Ali, S. H., Ahmad, S. A., & Grammer, K. (2021). The role of spectral power ratio in characterizing emotional EEG for gender identification. Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020, 334–338. https://doi.org/10.1109/IECBES48179.2021.9398737 DOI: 10.1109/iecbes48179.2021.9398737

[ref-3] Apicella, A., Arpaia, P., De Blasiis, P., Calce, A. Della, Fullin, A., Gargiulo, L., Maffei, L., Mancino, F., Moccaldi, N., Pollastro, A., & Vallefuoco, E. (2022). EEG-based system for Executive Function fatigue detection. 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings, 656–660. https://doi.org/10.1109/METROXRAINE54828.2022.9967542 DOI: 10.1109/metroxraine54828.2022.9967542

[ref-4] Chen, K., Chai, S., Xie, T., Liu, Q., & Ma, L. (2024). EEG spatial inter-channel connectivity analysis: A GCN-based dual stream approach to distinguish mental fatigue status. Artificial Intelligence in Medicine, 157, 102996. https://doi.org/10.1016/J.ARTMED.2024.102996 DOI: 10.1016/j.artmed.2024.102996

[ref-5] Chen, K., Li, Z., Ai, Q., Liu, Q., & Wang, L. (2021). An improved CNN model based on fused time-frequency features for mental fatigue detection in BCIs. IISA 2021 - 12th International Conference on Information, Intelligence, Systems and Applications. https://doi.org/10.1109/IISA52424.2021.9555518 DOI: 10.1109/iisa52424.2021.9555518

[ref-6] Cicchino, J. B. (2017). Effectiveness of forward collision warning and autonomous emergency braking systems in reducing front-to-rear crash rates. Accident Analysis & Prevention, 99, 142–152. https://doi.org/10.1016/J.AAP.2016.11.009 DOI: 10.1016/j.aap.2016.11.009

[ref-7] Cui, J., Lan, Z., Liu, Y., Li, R., Li, F., Sourina, O., & Müller-Wittig, W. (2022). A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG. Methods, 202, 173–184. https://doi.org/10.1016/J.YMETH.2021.04.017 DOI: 10.1016/j.ymeth.2021.04.017

[ref-8] Feng, X., Dai, S., & Guo, Z. (2025). Pseudo-label-assisted subdomain adaptation network with coordinate attention for EEG-based driver drowsiness detection. Biomedical Signal Processing and Control, 101, 107132. https://doi.org/10.1016/J.BSPC.2024.107132 DOI: 10.1016/j.bspc.2024.107132

[ref-9] Gupta, A., Agrawal, R. K., Kirar, J. S., Andreu-Perez, J., DIng, W. P., Lin, C. T., & Prasad, M. (2021). On the Utility of Power Spectral Techniques with Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI. IEEE Transactions on Systems,

[ref-10] Man, and Cybernetics: Systems, 51(5), 3080–3092. https://doi.org/10.1109/TSMC.2019.2917599 DOI: 10.1109/tsmc.2019.2917599

[ref-11] Jantan, S., Ahmad, S. A., Soh, A. C., Ishak, A. J., & Adnan, R. N. E. R. (2022). A Multi-Model Analysis for Driving Fatigue Detection using EEG Signals. 7th IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2022 - Proceedings, 183–188. https://doi.org/10.1109/IECBES54088.2022.10079534 DOI: 10.1109/iecbes54088.2022.10079534

[ref-12] Jiang, X., Bian, G. Bin, & Tian, Z. (2019). Removal of Artifacts from EEG Signals: A Review. Sensors (Basel, Switzerland), 19(5). https://doi.org/10.3390/S19050987 DOI: 10.3390/s19050987

[ref-13] Kaur, P., & Sobti, R. (2018). Sensor Fusion Algorithm for Software Based Advanced Driver-Assistance Intelligent Systems. ICSCCC 2018 - 1st International Conference on Secure Cyber Computing and Communications, 457–460. https://doi.org/10.1109/ICSCCC.2018.8703269 DOI: 10.1109/icsccc.2018.8703269

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[ref-15] Li, R., Wang, L., & Sourina, O. (2022). Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness. Methods, 202, 136–143. https://doi.org/10.1016/J.YMETH.2021.04.009 DOI: 10.1016/j.ymeth.2021.04.009

[ref-16] Liu, Q., Zhou, W., Zhang, Y., & Fei, X. (2021). Multi-target Detection based on Multi-sensor Redundancy and Dynamic Weight Distribution for Driverless Cars. 2021 IEEE 3rd International Conference on Communications, Information System and Computer Engineering, CISCE 2021, 229–234. https://doi.org/10.1109/CISCE52179.2021.9446002 DOI: 10.1109/cisce52179.2021.9446002

[ref-17] Liu, S., Wang, J., Li, S., & Cai, L. (2023). Epileptic Seizure Detection and Prediction in EEGs Using Power Spectra Density Parameterization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 3884–3894. https://doi.org/10.1109/TNSRE.2023.3317093 DOI: 10.1109/tnsre.2023.3317093

[ref-18] Nandavar, S., Kaye, S. A., Senserrick, T., & Oviedo-Trespalacios, O. (2023). Exploring the factors influencing acquisition and learning experiences of cars fitted with advanced driver assistance systems (ADAS). Transportation Research Part F: Traffic Psychology and Behaviour, 94, 341–352. https://doi.org/10.1016/J.TRF.2023.02.006 DOI: 10.1016/j.trf.2023.02.006

[ref-19] Paulo, J. R., Pires, G., & Nunes, U. J. (2021). Cross-Subject Zero Calibration Driver’s Drowsiness Detection: Exploring Spatiotemporal Image Encoding of EEG Signals for Convolutional Neural Network Classification. IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society, 29, 905–915. https://doi.org/10.1109/TNSRE.2021.3079505 DOI: 10.1109/tnsre.2021.3079505

[ref-20] Phan, A. C., Trieu, T. N., & Phan, T. C. (2023). Driver drowsiness detection and smart alerting using deep learning and IoT. Internet of Things, 22, 100705. https://doi.org/10.1016/J.IOT.2023.100705 DOI: 10.1016/j.iot.2023.100705

[ref-21] Pion-Tonachini, L., Kreutz-Delgado, K., & Makeig, S. (2019). ICLabel: An automated electroencephalographic independent component classifier, dataset, and website. NeuroImage, 198, 181. https://doi.org/10.1016/J.NEUROIMAGE.2019.05.026 DOI: 10.1016/j.neuroimage.2019.05.026

[ref-22] Pranoto, H., Wahab, A., Adriansyah, A., & Feriyanto, D. (2023). Internet of thing development for fatigue analyzer device control for truck and bus engine. AIP Conference Proceedings, 2671(1). https://doi.org/10.1063/5.0116314 DOI: 10.1063/5.0116314

[ref-23] Rahman, W., Ruman, M. R., Roushan Jahan, K., Roni, M. J., Foyjur Rahman, M., & Hasnat Shahriar, M. A. (2020). Vehicle

[ref-24] Speed Control and Accident Avoidance System Based on Arm M4 Microprocessor. 2020 International Conference on Industry 4.0 Technology, I4Tech 2020, 154–158. https://doi.org/10.1109/I4TECH48345.2020.9102693 DOI: 10.1109/i4tech48345.2020.9102693

[ref-25] Raveena, C. S., Sravya, R. S., Kumar, R. V., & Chavan, A. (2020). Sensor Fusion Module Using IMU and GPS Sensors for Autonomous Car. 2020 IEEE International Conference for Innovation in Technology, INOCON 2020. https://doi.org/10.1109/INOCON50539.2020.9298316 DOI: 10.1109/inocon50539.2020.9298316

[ref-26] Romahadi, D., Feleke, A. G., & Youlia, R. P. (2024). Evaluation of Laplacian Spatial Filter Implementation in Detecting Driver Vigilance Using Linear Classifier. International Journal of Technology, 15(6), 1712–1729. https://doi.org/10.14716/IJTECH.V15I6.7166 DOI: 10.14716/ijtech.v15i6.7166

[ref-27] Shen, M., Zou, B., Li, X., Zheng, Y., Li, L., & Zhang, L. (2021). Multi-source signal alignment and efficient multi-dimensional feature classification in the application of EEG-based subject-independent drowsiness detection. Biomedical Signal Processing and Control, 70, 103023. https://doi.org/10.1016/J.BSPC.2021.103023 DOI: 10.1016/j.bspc.2021.103023

[ref-28] Song, R., Horridge, P., Pemberton, S., Wetherall, J., Maskell, S., & Ralph, J. (2019). A Multi-Sensor Simulation Environment for Autonomous Cars. FUSION 2019 - 22nd International Conference on Information Fusion. https://doi.org/10.23919/FUSION43075.2019.9011278 DOI: 10.23919/fusion43075.2019.9011278

[ref-29] Spicer, R., Vahabaghaie, A., Bahouth, G., Drees, L., Martinez von Bülow, R., & Baur, P. (2018). Field effectiveness evaluation of advanced driver assistance systems. Traffic Injury Prevention, 19(sup2), S91–S95. https://doi.org/10.1080/15389588.2018.1527030 DOI: 10.1080/15389588.2018.1527030

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