EEG Analysis to Decode Tactile Sensory Perception Using Neural Techniques

Abstract

This chapter introduces a novel approach to examine the scope of tactile sensory perception as a possible modality of treatment of patients suffering from certain mental disorder using a Support Vector Machines with kernelized neural network. Experiments are designed to understand the perceptual difference of schizophrenic patients from normal and healthy subjects with respect to three different touch classes, including soft touch, rubbing, massaging and embracing and their three typical subjective responses. Experiments undertaken indicate that for normal subjects and schizophrenic patients, the average percentage accuracy in classification of all the three classes: pleasant/acceptable/unpleasant is comparable with their respective oral responses. In addition, for schizophrenic patients, the percentage accuracy for acceptable class is very poor of the order of below 12%, which for normal subjects is quite high (42%). Performance analysis reveals that the proposed classifier outperforms its competitors with respect to classification accuracy in all the above three classes.