Please use this identifier to cite or link to this item: http://repository.umac.mo/handle/10692/1118
Title: Gaussian mixture model based on genetic algorithm for brain-computer interfaces
Authors: Wang, Bo Yu
Wong, Chi Man (黃志文)
Wan, Feng (萬峰)
Mak, Peng Un (麥炳源)
Mak, Pui In (麥沛然)
Issue Date: Oct-2010
Publisher: IEEE
Citation: The 3rd International Congress on Image and Signal Processing (CISP'10), Oct. 2010, p. 4079-4083
Abstract: Gaussian mixture model (GMM) has been considered to model the EEG data for the classification task in braincomputer 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.
URI: http://hdl.handle.net/10692/1118
Keywords: Electroencephalogram
Genetic algorithm
Gaussian mixture model
Brain-computer interface
Access: View full-text via DOI

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