Evolving spatial and frequency selection filters for brain-computer interfaces

Proceeding of: 2010 IEEE World Congress in Computational Intelligence (WCCI 2010), Barcelona, Spain, July 18-23, 2010Abstract—Machine Learning techniques are routinely applied to Brain Computer Interfaces in order to learn a classifier for a particular user. However, research has shown that classiffication techniques perform better if the EEG signal is previously preprocessed to provide high quality attributes to the classifier. Spatial and frequency-selection filters can be applied for this purpose. In this paper, we propose to automatically optimize these filters by means of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). The technique has been tested on data from the BCI-III competition, because both raw and manually filtered datasets were supplied, allowing to compare them. Results show that the CMA-ES is able to obtain higher accuracies than the datasets preprocessed by manually tuned filters.This work has been funded by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR project)Publicad