EEG classification to determine the degree of pleasure levels in touch-perception of human subjects

Saha, A., Konar, A., Bhattacharya, Basabdatta and Nagar, A. K. (2015) EEG classification to determine the degree of pleasure levels in touch-perception of human subjects. In: International Joint Conference on Neural Networks 2015, 12-17 July 2015, Killarney, Ireland.


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This paper introduces a novel approach to examine the scope of touch perception as a possible modality of treatment of patients suffering from certain mental disorder using a Radial Basis function induced Back Propagation Neural Network. Experiments are designed to understand the perceptual difference of schizophrenic patients from normal and healthy subjects with respect to four different touch classes, including soft touch, rubbing, massaging and embracing and their three typical subjective responses such as pleasant, acceptable, and unpleasant. Experiments undertaken indicate that that the frontal part of the scalp map of healthy subjects carry more blood during touch perception than those obtained for the schizophrenic patients. Further, for normal subjects and schizophrenic patients, the average percentage accuracy in classification of all the three classes including pleasant, acceptable or 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 10, which for normal subjects is quite high (46). Performance analysis reveals that the proposed classifier outperforms its competitors with respect to classification accuracy in all the above three classes. A well known statistical test confirms that the proposed classifier outperforms all its competitors along with principal component analysis as feature selector by a large margin. © 2015 IEEE.

Keywords:Backpropagation, Electroencephalography, Functions, Neural networks, Patient treatment, Radial basis function networks, Torsional stress, Back propagation neural networks, Classification accuracy, EEG classification, Perceptual difference, Performance analysis, Radial basis functions, schizophrenia, Schizophrenic patients, Principal component analysis, JCNotOpen
Subjects:G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Engineering
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ID Code:20149
Deposited On:29 Jan 2016 11:31

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