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This paper presents an algorithm for simultaneous eye blink feature extraction and elimination from low-channel prefrontal EEG signals to improve driver drowsiness detection. The proposed method enhances detection accuracy by addressing eye blink artifacts that distort EEG signals, achieving a significant improvement in classification performance. The study validates the effectiveness of combining blink-related features with filtered EEG band power for more accurate driver state classification.

Author

23l204

Language

EN