Classification of EEG signals using Common Spatial Pattern-Principle Component Analysis and Interval Type-2 Fuzzy Logic System

Abstract

Brain Computer Interface, defined as a direct communication pathway between human brain and computer, allows a system to get the intention of the brain via Electroencephalogram (EEG) signals. This mechanism thus does not involve the participation of motoric and muscular neurons. In recent progresses, things such as the variability of imagery activities and subject characteristics were found to be the main problems toward the development of reliable signal translation methods. In this paper, we propose an EEG signal translation system based on motoric imagery activities. The system includes band-pass filter and Common Spatial Pattern (CSP) for noise filtering and Principle Component Analysis (PCA) for feature extraction. Interval Type-2 Fuzzy Logic System is then used as the classifier for the produced features. The later identified classes, either 0 or 1, refer to the imagery cursor movement direction either upward or downward respectively. The training and testing data that used here are from BCI Competition II dataset 1a. The highest classification accuracy of the system was recorded at 85.2%.