Fuzzy Inference System for Classification of Electroencephalographic (EEG) Data

Abstract

This paper aims to develop a Fuzzy Inference System that categorizes the Electroencephalographic (EEG) signals generated from a healthy brain with those generated by the brain suffering from epilepsy into different identifiable classes. This is done by statistical analysis of dynamical properties of the EEG signals using well established techniques for nonlinear time series analysis. For this purpose, a defined null hypothesis and obtained rejection counts are taken into consideration based on Randomness and Stationarity tests applied on the available EEG data. The outcome of this analysis is used to create a fuzzy inference model that can differentiate between brain states and zones they belong to viz. healthy zone, epileptic zone and non-epileptic zone. These zones are further classified into different states as eyes opened and closed for the healthy zone, the state where first ictal occurs, the seizure free interval and during the seizure activity within the epileptic zone and, the non focal and non epileptic state within the non–epileptic zone. Encouraging results have been obtained from this pilot project. However, limited data has been used for the development work and hence the decisions may be specific. Further generalization of the rules is possible with the help of more data and inputs from neurophysiological experts.