Szczegóły publikacji
Opis bibliograficzny
Fusion of EEG and eye blink analysis for detection of driver fatigue / Mohammad Shahbakhti, Matin Beiramvand, Erfan Nasiri, Somayeh MOHAMMADI FAR, Wei Chen, Jordi Solé-Casals, Michal Wierzchon, Anna BRONIEC-WÓJCIK, Piotr AUGUSTYNIAK, Vaidotas Marozas // IEEE Transactions on Neural Systems and Rehabilitation Engineering ; ISSN 1534-4320. — 2023 — vol. 31, s. 2037-2046. — Bibliogr. s. 2045-2046, Abstr. — Publikacja dostępna online od: 2023-04-14
Autorzy (10)
- Shahbakhti Mohammad
- Beiramvand Matin
- Nasiri Erfan
- AGHMohammadi Far Somayeh
- Chen Wei
- Solé-Casals Jordi
- Wierzchoń Michał
- AGHBroniec-Wójcik Anna
- AGHAugustyniak Piotr
- Marozas Vaidotas
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 147584 |
|---|---|
| Data dodania do BaDAP | 2023-07-27 |
| Tekst źródłowy | URL |
| DOI | 10.1109/TNSRE.2023.3267114 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Abstract
Objective: The driver fatigue detection using multi-channel electroencephalography (EEG) has been extensively addressed in the literature. However, the employment of a single prefrontal EEG channel should be prioritized as it provides users with more comfort. Furthermore, eye blinks from such channel can be analyzed as the complementary information. Here, we present a new driver fatigue detection method based on simultaneous EEG and eye blinks analysis using an Fp1 EEG channel. Methods: First, the moving standard deviation algorithm identifies eye blink intervals (EBIs) to extract blink-related features. Second, the discrete wavelet transform filters the EBIs from the EEG signal. Third, the filtered EEG signal is decomposed into sub-bands, and various linear and nonlinear features are extracted. Finally, the prominent features are selected by the neighbourhood components analysis and fed to a classifier to discriminate between fatigue and alert driving. In this paper, two different databases are investigated. The first one is used for parameters' tuning of proposed method for the eye blink detection and filtering, nonlinear EEG measures, and feature selection. The second one is solely used for testing the robustness of the tuned parameters. Main results: The comparison between the obtained results from both databases by the AdaBoost classifier in terms of sensitivity (90.2% vs. 87.4%), specificity (87.7% vs. 85.5%), and accuracy (88.4% vs. 86.8%) indicates the reliability of the proposed method for the driver fatigue detection. Significance: Considering the existence of commercial single prefrontal channel EEG headbands, the proposed method can be used to detect the driver fatigue in real-world scenarios. © 2001-2011 IEEE.