Spatial filter and feature selection optimization based on EA for multi-channel EEG

The EEG signals employed for BCI systems are generally band-limited. The band-limited multiple Fourier linear combiner (BMFLC) with Kalman filter was developed to obtain amplitude estimates of the EEG signal in a pre-fixed frequency band in real-time. However, the high-dimensionality of the feature vector caused by the application of BMFLC to multi-channel EEG based BCI deteriorates the performance of the classifier. In this work, we apply evolutionary algorithm (EA) to tackle this problem. The real-valued EA encodes both the spatial filter and the feature selection into its solution and optimizes it with respect to the classification error. Three BMFLC based BCI configurations are proposed. Our results show that the BMFLC-KF with covariance matrix adaptation evolution strategy (CMAES) has the best overall performance.