Rule extraction from electroencephalogram signals using support vector machine

Abstract

Emotion classification and recognition from electroencephalogram (EEG) signals have been studied extensively due to its potential benefits such as entertainment and health care. Concerning classification, various techniques have been developed and applied. Support Vector Machines (SVMs) has been reported as the most used because of its accuracy. Nevertheless, although SVMs has satisfactory performance, it is unable to provide explanation of the relationships between a model's inputs and outputs. Specifically, it is desirable for a medical application for diagnosis to provide comprehensible rules. Consequently, SVM might not be suitable. In this study, SVM is treated as a black-box and then rules are extracted using the Classification And Regression Trees (CART) approach. A dataset from the Database for Emotion Analysis using Physiological Signals (DEAP) is used in this study. The experimental results show that although a classic SVM model has provided the best accuracy, a rule extraction model from SVM output by CART (SVM-CART) is better than a basic CART model. Therefore, the proposed SVM-CART approach is suitable for applications which need explanations and comprehensibility, such as medical applications.