A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
BCI for Games: A `State of the Art' Survey
ICEC '08 Proceedings of the 7th International Conference on Entertainment Computing
Application notes: analogue evolutionary brain computer interfaces
IEEE Computational Intelligence Magazine
Presence: Teleoperators and Virtual Environments
A direct optimization approach to the P300 speller
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Combinatorial optimization by learning and simulation of Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Markov Networks in Evolutionary Computation
Markov Networks in Evolutionary Computation
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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.