Version 1
: Received: 23 May 2024 / Approved: 24 May 2024 / Online: 24 May 2024 (08:32:09 CEST)
How to cite:
Zayed, A.; Belhadj, N.; Ben Khalifa, K.; Bedoui, M. H.; Valderrama Sakuyama, C. A. Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes. Preprints2024, 2024051615. https://doi.org/10.20944/preprints202405.1615.v1
Zayed, A.; Belhadj, N.; Ben Khalifa, K.; Bedoui, M. H.; Valderrama Sakuyama, C. A. Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes. Preprints 2024, 2024051615. https://doi.org/10.20944/preprints202405.1615.v1
Zayed, A.; Belhadj, N.; Ben Khalifa, K.; Bedoui, M. H.; Valderrama Sakuyama, C. A. Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes. Preprints2024, 2024051615. https://doi.org/10.20944/preprints202405.1615.v1
APA Style
Zayed, A., Belhadj, N., Ben Khalifa, K., Bedoui, M. H., & Valderrama Sakuyama, C. A. (2024). Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes. Preprints. https://doi.org/10.20944/preprints202405.1615.v1
Chicago/Turabian Style
Zayed, A., Mohamed Hedi Bedoui and Carlos Alberto Valderrama Sakuyama. 2024 "Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes" Preprints. https://doi.org/10.20944/preprints202405.1615.v1
Abstract
Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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