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EEG Signal Multichannel Frequency-Domain Ratio Indices for Drowsiness Detection Based on Multicriteria Optimization.

Igor Stancin¹, Nikolina Frid¹, Mario Cifrek¹

¹Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, 10000 Zagreb, Croatia.

Sensors (Basel, Switzerland)

|October 26, 2021

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Detecting drowsiness is crucial for safety. New multichannel EEG ratio indices improve drowsiness detection accuracy and efficiency, outperforming existing methods.

Area of Science:

Neuroscience
Biomedical Engineering
Signal Processing

Background:

Drowsiness poses significant risks in safety-critical activities like driving and aviation.
Current methods for drowsiness detection lack reliable definitions and accurate systems.
Existing research links electroencephalogram (EEG) frequency-domain features to drowsiness, with ratio indices showing promise.

Purpose of the Study:

To develop novel multichannel ratio indices for improved drowsiness detection.
To leverage evolutionary algorithms for optimal feature and channel selection.
To enhance the precision and accuracy of drowsiness detection systems.

Main Methods:

Utilized an evolutionary metaheuristic algorithm to identify optimal EEG features and channels.

Keywords:

EEG drowsiness detection frequency-domain features machine learning multicriteria optimization

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Developed novel six-channel ratio indices based on frequency-domain features from different brain regions.

Compared the performance of new indices against existing single-channel indices using XGBoost classification.

Main Results:

Identified delta and alpha band powers as key indicators of drowsiness.
Novel six-channel indices demonstrated statistically significant improvements in distinguishing wakefulness from drowsiness.
Achieved enhanced precision, classification accuracy, and computational efficiency compared to prior methods.

Conclusions:

Multichannel frequency-domain EEG features offer superior drowsiness detection capabilities.
The findings highlight the need for a more precise definition of drowsiness.
Early and accurate drowsiness detection can be achieved using advanced multichannel EEG analysis.