Simultaneous Eye Blink Characterization and Elimination From Low-Channel Prefrontal EEG Signals Enhances Driver Drowsiness Detection

• Published in: IEEE journal of biomedical and health informatics Vol. 26; no. 3; pp. 1001 - 1012
• Main Authors: Shahbakhti, Mohammad, Beiramvand, Matin, Rejer, Izabela, Augustyniak, Piotr, Broniec-Wojcik, Anna, Wierzchon, Michal, Marozas, Vaidotas
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Blinking; Cognitive ability; Discrete Wavelet Transform; Discrete wavelet transforms; driver drowsiness; Driver fatigue; Drowsiness; EEG; Electrodes; Electroencephalography; eye blink; Feature extraction; High frequency; Humans; Pollution measurement; Principal components analysis; Sleepiness; Support vector machines; Vehicles; Wakefulness; Wavelet Analysis; Wavelet transforms
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2021.3096984

Objective: Blink-related features derived from electroencephalography (EEG) have recently arisen as a meaningful measure of driver's cognitive state. Combined with band power features of low-channel prefrontal EEG data, blink-derived features enhance the detection of driver drowsiness. Yet, it remains unanswered whether synergy of combined blink and EEG band power features for the detection of driver drowsiness may be further boosted if a proper eye blink removal is also applied before EEG analysis. This paper proposes an algorithm for simultaneous eye blink feature extraction and elimination from low-channel prefrontal EEG data. Methods: Firstly, eye blink intervals (EBIs) are identified from the Fp1 EEG channel using variational mode extraction, and then blink-related features are derived. Secondly, the identified EBIs are projected to the rest of EEG channels and then filtered by a combination of principal component analysis and discrete wavelet transform. Thirdly, a support vector machine with 10-fold cross-validation is employed to classify alert and drowsy states from the derived blink and filtered EEG band power features. Main results: When compared the synergy of eye blink and EEG features before and after filtering by the proposed algorithm, a significant improvement in the mean accuracy of driver drowsiness detection was achieved (71.2% vs. 78.1%, p <inline-formula><tex-math notation="LaTeX">< </tex-math></inline-formula> 0.05). Significance: This paper validates a novel view of eye blinks as both a source of information and artifacts in EEG-based driver drowsiness detection.