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Evolutionary Design of a Brain-Computer Interface

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Computational Intelligence and Bioinspired Systems (IWANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3512))

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Abstract

This paper shows how Evolutionary Algorithm (EA) robustness help to solve a difficult problem with a minimal expert knowledge about it. The problem consist in the design of a Brain-Computer Interface (BCI), which allows a person to communicate without using nerves and muscles. Input electroencephalographic (EEG) activity recorded from the scalp must be translated into outputs that control external devices. Our BCI is based in a Multilayer Perceptron (MLP) trained by an EA. This kind of training avoids the main problem of MLPs training algorithms: overfitting. Experimental results produceMLPs with a classification ability better than those in the literature.

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Romero, G., Arenas, M.G., Castillo, P.A., Merelo, J.J. (2005). Evolutionary Design of a Brain-Computer Interface. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_82

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  • DOI: https://doi.org/10.1007/11494669_82

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26208-4

  • Online ISBN: 978-3-540-32106-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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