Decimation filter with Common Spatial Pattern and Fishers Discriminant Analysis for motor imagery classification

Abstract

Brain Computer Interface (BCI) system converts thoughts into commands for driving external device with Electroencephalography (EEG). This paper presents the use of decimation filters for filtering the EEG signal. Common Spatial Pattern (CSP) technique is used to transform the filtered signal to a new time series in order to have optimal variance for the discrimination of different tasks. Fishers Discriminant Analysis (FDA) is applied to the CSP features and the FDA scores are fed to a Support Vector Machine (SVM) classifier. The method is evaluated on BCI Competition III Dataset IVa and compared with other related state-of-the-art approaches. The results show that our method outperforms all other approaches in terms of average classification error rate. Compared to best performing method that uses only CSP features, the results obtained in this research offer on average a reduction of 1.07% in the classification error rate.