An EEG-based cognitive failure detection in driving using two-stage motor intension classifier

Cognitive failure of drivers during driving becomes nowadays the most alarming issue for traffic fatalities, and hence, it is important to intend correct motor actions during driving to avoid accidents. This paper proposes a novel two-stage motor intension classifier to detect the correct motor planning of the drivers during car driving. Common spatial pattern filters are introduced here for feature extraction as well as artifact removal from the raw electroencephalographic signals. Experiments undertaken further confirm that the basic motor intensions including i) steering control, ii) accelerator, iii) brake, and iv) no action and as well as the sub-classification of individual control are best classified by Support vector machine with polynomial kernel from a list of standard classifiers with an average classification accuracy of 85.70%.