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Evolving spatial and frequency selection filters for brain-computer interfaces

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URI: http://hdl.handle.net/10016/8070
DOI: 10.1109/CEC.2010.5586383
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evolving_aler_WCCI 2010.pdf (605.52 KB)
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2010-07
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Abstract—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.
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Proceeding of: 2010 IEEE World Congress in Computational Intelligence (WCCI 2010), Barcelona, Spain, July 18-23, 2010
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Brain computer interface, Evolution of filters
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2010 IEEE Congress on Evolutionary Computation, 2010, pp.1-7
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