Applying evolution strategies to preprocessing EEG signals for þÿ b r a i n c o m p u t e r i n t e r f a c e s Among them, spatial filters and selecting the most appropriate frequency-bands in the frequency domain are known to improve classification accuracy. However, because of the high variability among users, the filters must be properly adjusted to every user's data before competitive results can be obtained. In this paper we propose to use the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for automatically tuning the filters. Spatial and frequency-selection filters are evolved to minimize both classification error and the number of frequency bands used. This evolutionary approach to filter optimization has been tested on data for different users from the BCI-III competition. The evolved filters provide higher accuracy than approaches used in the competition. Results are also consistent across different runs of CMA-ES.
In REF a system has been proposed to use the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for automatically tuning the filters.
Applying evolution strategies to preprocessing EEG signals for brain-computer interfaces
Inf. Sci.
Inf. Sci.
Computer Science