Development of driver drowsiness classification based on EEG signals using long short-term memory method
1
Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia
2
Biomedical Engineering Innovation Research, Universitas Airlangga, Surabaya, Indonesia
3
Faculty of Engineering, Universiti Malaysia Sarawak, Malaysia
Submission date: 2025-11-24
Final revision date: 2026-06-25
Acceptance date: 2026-06-30
Online publication date: 2026-06-30
Publication date: 2026-06-30
Corresponding author
Diagnostyka 2026;27(2):2026215
KEYWORDS
TOPICS
ABSTRACT
Driving accidents have a high prevalence cause of death with 1.3 million fatalities each year. Drowsy driving accounts for 26% of these accidents. A classification system for driver drowsiness was developed as a preventive countermeasure for the issue. The classification system was developed using Electroencephalograph (EEG) data recorded from four test subjects aged 19-24 during Virtual Reality (VR) driving simulations. The system categorizes drowsiness into four classes: awake, and three levels of drowsiness: mild, moderate, and severe, based on the reaction times measured during the Psychomotor Vigilance Test (PVT). EEG signals were processed using Discrete Wavelet Transform (DWT) to extract alpha, beta, and theta wave features. These features were then analyzed statistically using means and energy. This study compared the performance of four variations of Long Short-Term Memory (LSTM) models as a classification system. The most effective model was an LSTM with six hidden layers and 50 units per layer, achieving a test accuracy of 91% with the highest precision, sensitivity and F1-score, and accuracy average values of 0.93, 0.91, 0.92 across all classes.
FUNDING
This study was funded by The Directorate of Research, Technology, and Community Service (DRTPM) on behalf of the Ministry of Education, Culture, Research, and Technology under the BIMA program (Contract: 040/E5/PG.02.00.PL/2024;1754/B/UN3.LPPM/PT.01.03/2024) and the Faculty Of Science And Technology, Universitas Airlangga. The authors thank them for their financial and technical support throughout this research.
REFERENCES (22)
1.
Indonesia BPS. Jumlah Kecelakaan, Korban Mati, Luka Berat, Luka Ringan, dan Kerugian Materi - Tabel Statistik [Internet]. www.bps.go.id. 2024. Indonesia Available (29.06.2026) from: https://www.bps.go.id/id/stati....
2.
Rani Rahmadiyani, Ari Widyanti. Prevalence of drowsy driving and modeling its intention: An Indonesian case study. Transportation research interdisciplinary perspectives. 2023; 1;19: 100824–4.
3.
Soares S, Monteiro T, Lobo A, Couto A, Cunha L, Ferreira S. Analyzing Driver Drowsiness: From Causes to Effects. Sustainability. 2020;5;12(5):1971.
4.
Chen F, Terken J. Automotive Interaction Design. Springer tracts in mechanical engineering. 2023.
5.
Gibbings A, Ray LB, Berberian N, Nguyen T, Shahidi Zandi A, Owen AM, Comeau FJE, Fogel SM. EEG and behavioural correlates of mild sleep deprivation and vigilance. Clin Neurophysiol.2021;132(1):45-55. https://doi.org/10.1016/j.clin....
6.
Chai M, Li S-w, Sun W-c, Guo M-z. Drowsiness monitoring based on steering wheel status. Transp Res D Transp Environ. 2018;66:95-104. https://doi.org/10.1016/j.trd.....
7.
Barua B, Biswas S, Deb K. An efficient method of lane detection and tracking for highway safety. In: 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT). Dhaka, Bangladesh: IEEE; 2019.:1-6. https://doi.org/10.1109/ICASER....
8.
Maheswari VU, Aluvalu R, Kantipudi M P, Chennam K K, Kotecha K, Saini JR. Driver drowsiness prediction based on multiple aspects using image processing techniques. IEEE Access. 2022;10:54980–54990. https://doi.org/10.1109/ACCESS....
9.
Wang X, You L, Chen J, Han S. The impact of different service states of tunnel lighting on traffic safety. Accid Anal Prev. 2023;192:107237. https://doi.org/10.1016/j.aap.....
10.
Wang L, Wang H, Liu J. Discrimination of driver fatigue based on distortion energy density theory and multiple physiological signals. IEEE Access. 2021;9:145123-145134. https://doi.org/10.1109/ACCESS....
11.
Iwamoto H, Hori K, Fujiwara K, Kano M. Real-driving-implementable drowsy driving detection method using heart rate variability based on long short-term memory and autoencoder. IFAC-PapersOnLine. 2021;54(15):526-531. https://doi.org/10.1016/j.ifac....
12.
Rahma ON, Rahmatillah A. Drowsiness analysis using common spatial pattern and extreme learning machine based on electroencephalogram signal. J Med Signals Sens. 2019;9(2):130-136. https://doi.org/10.4103/JMSS.J....
13.
Stephan AM, Lecci S, Cataldi J, Siclari F, et al. Conscious experiences and high-density EEG patterns predicting subjective sleep depth. Curr Biol. 2021;31(24):5171-5182.e4. https://doi.org/10.1016/j.cub.....
14.
Filio D, Ziraldo E, Dony L, Gonzalez D, Oliver M. Comparison between wrap around screens and a head-mounted display on driver muscle and kinematic responses to a pedestrian hazard. Appl Ergon. 2023;106:103878. https://doi.org/10.1016/j.aper....
15.
Bilgin P, Agres K, Robinson N, Wai AAP, Guan C. A comparative study of mental states in 2D and 3D virtual environments using EEG. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC). Bari, Italy: IEEE; 2019: 2833-2838. https://doi.org/10.1109/SMC.20....
16.
Hartfiel B, Stark R. Validity of primary driving tasks in head-mounted display-based driving simulators. Virtual Real. 2021;25:819-833. https://doi.org/10.1007/s10055....
17.
Beck M, Pöppel K, Spanring M, Auer A, Prudnikova O, Kopp M, Klambauer G, Brandstetter J, Hochreiter S. xLSTM: Extended Long Short-Term Memory. arXiv 2024 May 7 Available from: https://doi.org/10.48550/arXiv....
18.
Kushida CA, Littner M, Morgenthaler T, Alessi C, Bailey D, Coleman Jr J, Hirshkowitz M, Kapen S, Kramer M, Lee-Chiong T, et al. Atlas of Clinical Sleep Medicine. 3rd ed. Philadelphia (PA): Elsevier; 2023. https://doi.org/10.1016/C2018-....
19.
Gao Z, et al. EEG-based spatio–temporal convolutional neural network for driver fatigue evaluation. IEEE Trans Neural Netw Learn Syst. 2019;30(9):2755-2763. https://doi.org/10.1109/TNNLS.....
20.
Aidman E, Balin M, Johnson K, et al. Caffeine may disrupt the impact of real-time drowsiness on cognitive performance: a double-blind, placebo-controlled small-sample study. Sci Rep. 2021;11:4027. https://doi.org/10.1038/s41598....
21.
Simanjuntak RA, Oesman TI, et al. Analisis hubungan faktor-faktor penyebab kelelahan kerja pada aktivitas pengolahan benih padi. In: Peningkatan Daya Saing Melalui Perbaikan Mutu Produk dalam Rangka Pembangunan Pertanian di Era Industri 4.0. Yogyakarta: Fakultas Agroindustri, Universitas Mercu Buana Yogyakarta; 2020:709–722. Indonesia Available (29.06.2026) from: https://eprints.akprind.ac.id/....
22.
Phadikar S, Sinha N, Ghosh R. Automatic eyeblink artifact removal from EEG signal using wavelet transform with heuristically optimized threshold. IEEE J Biomed Health Inform. 2021;25(2):475-484. https://doi.org/10.1109/JBHI.2....
Share
RELATED ARTICLE
| eISSN: | 2449-5220 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
