Classification of EEG Signal using Correlation Coefficient among Channels as Features Extraction Method

Abstract

Objectives: In this paper, we have evaluated the effectiveness of classification of Electroencephalogram (EEG) signals using the correlation between channels as a method of features selection. Methods/Statistical Analysis: First data is broken sample wise, then correlation coefficient between channel pair for each sample is calculated. After that mean of the correlation coefficient of all channel pair for each class over all samples is calculated and in a similar manner, standard deviation from the mean is also calculated. For feature selection we have plotted a pair of the Gaussian curves between channels of two separate classes and choose those channels which give us lower misclassified area as features. Then these features are used for training purpose of Support Vector Machine (SVM). Findings: Most of the previous researches follow either signal processing approach or machine learning approach while we emphasized upon the nature of the signal propagation amongst the neurons. The basic idea behind the feature selection is taken from the way the signals propagate from one neuron to the other. In our work we assume that EEG signals follow the normal distribution and verify the fact using chi-square test. On applying SVM the accuracy of classification on testing data confirms that correlation among channels can be used for feature selection. Application/Improvements: The results can be improved by improving the pre-processing of EEG signals. It can be used to develop a Brain Computer Interaction (BCI) system.