The Improvement of a Brain Computer Interface Based on EEG Signals

Abstract

Purpose: Brain Computer Interface (BCI) has provided a novel way of communication that can significantly revolutionize life of people suffering from disabilities. Motor Imagery (MI) EEG BCI is one of the most promising solutions to address. The main phases of such systems include signal acquisition, pre-processing, feature extraction, classification and the intended interface. The challenging obstacles in such systems are to detect and extract efficient features that present reliability and robustness alongside promising classification accuracy. In this paper it is endeavored to present a robust method for a two-class MI BCI that results in high accuracy. Materials and Methods: For this purpose, the dataset 2b from BCI competition 2008, consisting of three channels (C3, C and Cz), was utilized. Firstly, the signals were bandpass filtered. Secondly, Common Spatial Pattern (CSP) was employed and then a number of features, including non-linear chaotic features were extracted from channels C3 and C4. After feature selection phase the number of features were reduced to 38 and 47. Finally, these features were fed into two classifiers, namely Support Vector Machine classifier (SVM) and Bagging to evaluate the performance of the system. Results: Classification accuracy and Cohen’s Kappa coefficient of the proposed method for two MI EEG channels are 96.40% and 0.92, respectively. Conclusion: These results indicate the high accuracy and stability of our method in comparison with similar studies. Therefore, it can be a promising approach in two-class MI BCI systems.