Introducing the use of model-based evolutionary algorithms for EEG-based motor imagery classification

Introducing the use of model-based evolutionary algorithms for EEG-based motor imagery classification” by R. Santana, L. Bonnet, J. Légeny, and A. Lécuyer. In Proceedings of the 2012 Genetic and Evolutionary Computation Conference GECCO-2012, (Philadelphia, US), 2012, pp. 1159-1166.

Abstract

Brain computer interfaces (BCIs) allow the direct human-computer interaction without the need of motor intervention. To properly and efficiently decode brain signals into computer commands the application of machine-learning techniques is required. Evolutionary algorithms have been increasingly applied in different steps of BCI implementations. In this paper we introduce the use of the covariance matrix adaptation evolution strategy (CMA-ES) for BCI systems based on motor imagery. The optimization algorithm is used to evolve linear classifiers able to outperform other traditional classifiers. We also analyze the role of modeling variables interactions for additional insight in the understanding of the BCI paradigms.

BibTeX entry:

@inproceedings{Santana_et_al:2012c,
   author = {R. Santana and L. Bonnet and J. L{\'e}geny and A. L{\'e}cuyer},
   title = {Introducing the use of model-based evolutionary algorithms for
	{EEG}-based motor imagery classification},
   booktitle = {Proceedings of the 2012 Genetic and Evolutionary
	Computation Conference GECCO-2012},
   pages = {1159--1166},
   publisher = {ACM Press},
   address = {Philadelphia, US},
   year = {2012},
   url = {http://dl.acm.org/citation.cfm?id=2330323}
}

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