Discriminative FilterBank selection and EEG information fusion for Brain Computer Interface

Brain computer interface (BCI) provides a direct communication pathway between a human and an external device. In this paper, we propose a new dasiadiscriminative filterbank common spatial pattern (DFBCSP)psila algorithm to select the subject-specific filters automatically during training for a motor imagery based BCI. The subject-specific filters are selected using the fisher ratio values of filtered electroencephalogram (EEG) signal. The channel dasiaC3psila alone could give sufficient information to select the discriminative filterbank for the proposed system. We have also explored the possibility of boosting the system performance by including dynamic temporal features. Fusion of static and dynamic features in the proposed DFBCSP frame work gave an average test accuracy of 92.44%, which is significantly better than conventional filterbank based common spatial pattern algorithms.