Can SVM be used for automatic EEG detection of drowsiness during car driving?

Abstract

This study aims to develop an automatic method to detect drowsiness onset while driving. Support vector machines (SVM) represents a superior signal classification tool based on pattern recognition. The usefulness of SVM in identifying and differentiating electroencephalographic (EEG) changes that occur between alert and drowsy states was tested. Twenty human subjects underwent driving simulations with EEG monitoring. Alert EEG was marked by dominant beta activity, while drowsy EEG was marked by alpha dropouts. The duration of eye blinks corresponded well with alertness levels associated with fast and slow eye blinks. Samples of EEG data from both states were used to train the SVM program by using a distinguishing criterion of 4 frequency features across 4 principal frequency bands. The trained SVM program was tested on unclassified EEG data and subsequently checked for concordance with manual classification. The classification accuracy reached 99.3%. The SVM program was also able to predict the transition from alertness to drowsiness reliably in over 90% of data samples. This study shows that automatic analysis and detection of EEG changes is possible by SVM and SVM is a good candidate for developing pre-emptive automatic drowsiness detection systems for driving safety.