Combining CCA and CFP for Enhancing the Performance in the Hybrid BCI System

Hybrid Brain Computer Interface (BCI) is gaining attention as it can provide better performance or increase the number of user commands to control an external device. Hybrid BCI system using Motor imagery (MI) and Steady-state visually evoked potential (SSVEP) is one such system. Maintaining the performance during channel reduction is important in practical applications. In this paper we propose a combined feature extraction method using Canonical Correlation Analysis (CCA) and Common Frequency Pattern (CFP) method, where the features obtained from these methods were combined for classification. We used LDC and PARZEN for estimating the classification accuracy for the proposed method and individual method. Highest accuracy of 96.1 % is obtained for combined feature method (CCA+CFP). Whereas, the accuracy is 89.6% with CCA and 91.6% with CFP method. A significance test has shown that the performance of the proposed method is significantly different from both the individual methods (p <; 0.05).