A discriminative subject-specific spatio-spectral filter selection approach for EEG based motor-imagery task classification

Motor-imagery tasks generate event related synchronization and de-synchronization in certain subject specific frequency ranges of the subject's ElectroEncephaloGraphy (EEG) signals. The selection of frequency ranges for each subject is important for obtaining better classification accuracy of motor-imagery based Brain Computer Interface (BCI). Further, the spatial filters extracted corresponding to the selected spectral ranges also influence the classification accuracy. In this paper, a subject-specific spatio-spectral filter selection approach using a cognitive fuzzy inference system for classification of the motor-imagery tasks in a two step approach is presented. The cognitive fuzzy inference system (CFIS) employs an evolving interval type-2 system to classify the non-stationary features. The classifier employs a meta-cognitive sequential algorithm to determine both the structure and parameters of the CFIS. In the first step, the CFIS classifier is used to find the desired spectral filters by eliminating those frequency bands that do not affect the classification performance. In the second step, CFIS is used to eliminate those spatial filters which do not affect the performance. The performance of CFIS based spatio-spectral scheme has been evaluated using two publicly available BCI competition data sets and compared with other existing algorithms like FBCSP, DCSP and BSSFO. The results indicate that the proposed approach outperforms the CSP method by approximately 15-18% and other algorithms like FBCSP, DCSP by 8-10%. Compared to a recently proposed algorithm BSSFO, it achieves an improvement of 2%, but is simpler in comparison to BSSFO. The main impact of the work is its ability to handle non-stationarity using interval type-2 sets and provide good classification performance. In general, the proposed CFIS algorithm can be applied in the field of expert and intelligent systems where it is necessary to deal with non-stationary signals. (C) 2016 Elsevier Ltd. All rights reserved.