A fuzzy genetic algorithm to optimize spatial filter selection can improve the performance of P300-based brain computer interfaces (BCI); genetic algorithm searches an optimal configuration supported by a fuzzy inference system, it would reduce the error calculated during a 4 fold crossvalidation. The performance is measured through the accuracy and the bit rate, 4 methods based on fuzzy logic and Bayesian linear discriminant analysis are considered for the performance comparison. This proposed method has obtained significant results for healthy persons and post stroke patients, accuracies above 90% and bit rates greater than 8 bits/min for the most of cases evaluated in a P300-based BCI using the Hoffman approach.
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