A robust interval Type-2 Fuzzy Inference based BCI system

This paper presents a BCI system which addresses the key problems of robust feature extraction, non-stationarity and subject-specific spectral filter selection. It employs the Robust Common Spatial pattern (RoCSP) feature extraction algorithm which eliminates trials affected by artifacts and discards redundant channels to improve the robustness of the CSP algorithm. Next, it handles the non-stationarity in EEG signals using the Self-Regulated Interval Type-2 Neuro-Fuzzy Inference System (SRIT2NFIS). It uses the input features generated by the RoCSP algorithm and handles the non-stationarity as uncertainty using the interval type-2 fuzzy sets in the antecedent of fuzzy rules. A five layered modified Takagi-Sugeno-Kang interval type-2 fuzzy inference mechanism forms the structure and the learning algorithm uses a self-regulatory mechanism. Further, the SRIT2NFIS classifier is used to find the desired spectral filters by eliminating those frequency bands that do not affect the classification performance. The performance of the proposed system has been evaluated using two publicly available BCI competition data sets and compared with other existing algorithms like FBCSP, DFBCSP and BSSFO. The results indicate improved performances of the proposed algorithm. Finally, the proposed system is employed to control the movement of a quadcopter.