Motor imagery classification based on variable precision multigranulation rough set and game theoretic rough set

Abstract

In this work classification of motor imagery BCI based on Variable Precision Multigranulation Rough Set and Game theoretic Rough Set is proposed. The efficient classification of motor imagery movements of patients can lead to accurate design of Brain Computer Interface (BCI). Data set are collected from BCI Competition III dataset 3a and BCI competition IV data set I. During acquisition there are several noises that affect classification of Electroencephalogram (EEG) Signal, so pre-processing is carried out with Chebyshev type1 filter between 4–40 Hz in order to remove the noises that may exist in signal. The Daubechies wavelet is used for extraction of features from EEG Signal. Variable Precision Multigranulation Rough Set is applied for classification of EEG Signal. Game theoretic Rough set is applied to determine best combination of a and b are based on accuracy of Variable Precision Multigranulation Rough Set. An experimental result depicts higher accuracy with Variable Precision Multigranulation Rough Set and Game Theoretic rough set compared to existing technique.