Gaussian mixture model based on genetic algorithm for brain-computer interface

Gaussian mixture model (GMM) has been considered to model the EEG data for the classification task in brain-computer interface (BCI) system. In the practical BCI application, however, the performance of the classical GMM optimized by standard expectation-maximization (EM) algorithm may be degraded due to the noise and outliers, which often exist in realistic BCI systems. The motivation of this paper is to introduce the GMM based on the combination between the genetic algorithm (GA) and EM method to give a probabilistic output for further analysis and, more important, to achieve the reliable estimation by pruning the potential outliers and noisy samples in the EEG data, so the performance of BCI system can be improved. Experiments on two BCI datasets demonstrate the improvement in comparison with the classical mixture model.