Evolutionary design of a brain-computer interface

Authors:
G. Romero;M. G. Arenas;P. A. Castillo;J. J. Merelo
Affiliations:
Department of Architecture and Computer Technology, University of Granada, ETSII, Granada, Spain;Department of Computer Science, University of Jaén, EPS, Jaén, Spain;Department of Architecture and Computer Technology, University of Granada, ETSII, Granada, Spain;Department of Architecture and Computer Technology, University of Granada, ETSII, Granada, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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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.