Vehicle driver attention tracking during driving based on analysis of brainwaves
Road safety remains a critical global issue, with driver distraction and drowsiness identified as leading causes of vehicle accidents. In Malaysia, about 532,125 road accidents were reported from January to October 2024, with 5364 fatal accidents. Hence, this study presents the development of a vehi...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
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IEEE Xplore
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/45961/ |
| _version_ | 1848827535515713536 |
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| author | Muhammad Hazlami, Zolkafli Norizam, Sulaiman Mahfuzah, Mustafa Hasan, Md Mahmudul |
| author2 | SULAIMAN, NORIZAM |
| author_facet | SULAIMAN, NORIZAM Muhammad Hazlami, Zolkafli Norizam, Sulaiman Mahfuzah, Mustafa Hasan, Md Mahmudul |
| author_sort | Muhammad Hazlami, Zolkafli |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Road safety remains a critical global issue, with driver distraction and drowsiness identified as leading causes of vehicle accidents. In Malaysia, about 532,125 road accidents were reported from January to October 2024, with 5364 fatal accidents. Hence, this study presents the development of a vehicle driver attention tracking system based on the analysis of brainwaves or Electroencephalogram (EEG) signals to enhance the safety driving while driving at the road. The proposed system utilizes the Unicorn Hybrid Black EEG device and LabVIEW software to monitor and classify driver attention states while driving vehicle. The attention states are Focus, Normal, and Drowsy. Raw EEG signals are preprocessed using band-pass filters to reduce noise and artifacts, followed by feature extraction technique to extract EEG features in term of mean, standard deviation, and spectral entropy. Then, the selected EEG features are fed to machine learning such as K-Nearest Neighbor (KNN) classifier where the classification accuracy exceeding 90 % for detecting driver attention levels during driving vehicle. This research combines advanced EEG signal processing and machine learning classification to create a promising approach to reduce the likelihood of accident caused by lack of attention or drowsiness during driving vehicle. |
| first_indexed | 2025-11-15T04:02:16Z |
| format | Conference or Workshop Item |
| id | ump-45961 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:02:16Z |
| publishDate | 2025 |
| publisher | IEEE Xplore |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-459612025-10-22T08:11:04Z https://umpir.ump.edu.my/id/eprint/45961/ Vehicle driver attention tracking during driving based on analysis of brainwaves Muhammad Hazlami, Zolkafli Norizam, Sulaiman Mahfuzah, Mustafa Hasan, Md Mahmudul TK Electrical engineering. Electronics Nuclear engineering Road safety remains a critical global issue, with driver distraction and drowsiness identified as leading causes of vehicle accidents. In Malaysia, about 532,125 road accidents were reported from January to October 2024, with 5364 fatal accidents. Hence, this study presents the development of a vehicle driver attention tracking system based on the analysis of brainwaves or Electroencephalogram (EEG) signals to enhance the safety driving while driving at the road. The proposed system utilizes the Unicorn Hybrid Black EEG device and LabVIEW software to monitor and classify driver attention states while driving vehicle. The attention states are Focus, Normal, and Drowsy. Raw EEG signals are preprocessed using band-pass filters to reduce noise and artifacts, followed by feature extraction technique to extract EEG features in term of mean, standard deviation, and spectral entropy. Then, the selected EEG features are fed to machine learning such as K-Nearest Neighbor (KNN) classifier where the classification accuracy exceeding 90 % for detecting driver attention levels during driving vehicle. This research combines advanced EEG signal processing and machine learning classification to create a promising approach to reduce the likelihood of accident caused by lack of attention or drowsiness during driving vehicle. IEEE Xplore SULAIMAN, NORIZAM 2025-10-14 Conference or Workshop Item PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/45961/1/IEEE_INECCE2025_Published.pdf Muhammad Hazlami, Zolkafli and Norizam, Sulaiman and Mahfuzah, Mustafa and Hasan, Md Mahmudul (2025) Vehicle driver attention tracking during driving based on analysis of brainwaves. In: IEEE 8th International Conference on Electrical, Control and Computer Engineering, InECCE 2025 - Proceedings. IEEE 8th International Conference on Electrical, Control and Computer Engineering (InECCE 2025) , 27 - 28 August 2025 , Kuantan, Pahang. pp. 297-302.. ISBN 979-833152023-6 (Published) https://doi.org/10.1109/InECCE64959.2025.11150896 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Muhammad Hazlami, Zolkafli Norizam, Sulaiman Mahfuzah, Mustafa Hasan, Md Mahmudul Vehicle driver attention tracking during driving based on analysis of brainwaves |
| title | Vehicle driver attention tracking during driving based on analysis of brainwaves |
| title_full | Vehicle driver attention tracking during driving based on analysis of brainwaves |
| title_fullStr | Vehicle driver attention tracking during driving based on analysis of brainwaves |
| title_full_unstemmed | Vehicle driver attention tracking during driving based on analysis of brainwaves |
| title_short | Vehicle driver attention tracking during driving based on analysis of brainwaves |
| title_sort | vehicle driver attention tracking during driving based on analysis of brainwaves |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/45961/ https://umpir.ump.edu.my/id/eprint/45961/ |