The feasibility of real‐time drowsiness detection using commercially available, off‐the‐shelf, lightweight, wearable electroencephalogram (EEG) sensors is explored. While EEG signals are known to be reliable indicators of fatigue and drowsiness, they have not been used widely due to their size and form factor. However, the use of lightweight wearable EEGs alleviates this concern. Spectral analysis of EEG signals from these sensors using support vector machines (SVMs) is shown to classify drowsy states with high accuracy. The system is validated using data collected on 23 subjects in fresh and drowsy states. An accuracy of 81% is obtained at a per‐subject level and 74% in cross‐subject validation using SVM with radial basis kernel. Using a temporal aggregation strategy, the cross‐subject validation accuracy is shown to improve to 87%. The EEG signals are also used to characterise the blink duration and frequency of subjects. However, classification of drowsy states using blink analysis is shown to have lower accuracy than that using spectral analysis.
Real‐time drowsiness detection using wearable, lightweight brain sensing headbands
IET Intelligent Transport Systems ; 11 , 5 ; 255-263
2017-06-01
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
real‐time drowsiness detection , blink duration , wearable EEG sensors , cross‐subject validation , spectral analysis , temporal aggregation strategy , signal classification , signal detection , wearable electroencephalogram sensors , electroencephalography , support vector machines , EEG signals , drowsy state classification , radial basis kernel , blink analysis , wearable brain sensing headbands , SVM
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