PSO-SVM Identification Model for Driving Sustained Attention Level Based on EEG

In order to recognize driving sustained attention effectively,an identification method for sustained attention level was proposed based on the signal of electroencephalograph( EEG). Firstly,taking the driver's reaction time to random events as indexes,a dividing method for sustained attention levels was proposed. Secondly,using average spectrum amplitude from the bands of( θ( 4 ~ 8 Hz),α( 8~ 13 Hz),β( 13 ~ 30 Hz)) of EEG and its' ration value( α + β) / β,α / β,( θ + α) /( α + β),θ / β and( α + β) /θ as characteristic indexes,combining the particle swarm optimization( PSO) with support vector machine( SVM),an identification model for identifying sustained attention level was proposed.Finally,based on the data from driving simulating,the identification model was tested. The result shows that the average accuracy rate of model is 93. 02% and the method is applicable to identification of driving sustained attention level.