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|Title: ||Evolving spatial and frequency selection filters for brain-computer interfaces|
|Author(s): ||Aler, Ricardo|
Galván, Inés M.
Valls, José M.
|Issued date: ||Jul-2010|
|Citation: ||2010 IEEE Congress on Evolutionary Computation, 2010, pp.1-7|
|Description: ||Proceeding of: 2010 IEEE World Congress in Computational Intelligence (WCCI 2010), Barcelona, Spain, July 18-23, 2010|
|Abstract: ||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.|
|Sponsor: ||This work has been funded by the Spanish Ministry of Science under contract TIN2008-06491-C04-03 (MSTAR project)|
|Publisher version: ||http://dx.doi.org/10.1109/CEC.2010.5586383|
|Keywords: ||Brain computer interface|
Evolution of filters
|Rights: ||© IEEE|
|Appears in Collections:||DI - GCERN - Capítulos de Monografías|
DI - GCERN - Comunicaciones en Congresos y otros eventos
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