Pattern recognition for brain-computer interfaces by combining support vector machine with adaptive genetic algorithm

Authors:
Banghua Yang;Shiwei Ma;Zhihua Li
Affiliations:
Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, Shanghai University, Shanghai, China;Shanghai Key Laboratory of Power Station Automation Technology, Department of Automation, Shanghai University, Shanghai, China
Venue:
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Year:
2007

Citing 3
Cited 2

Adaptive brain interfaces

Communications of the ACM
Automated feature selection based on an adaptive genetic algorithm for brain-computer interfaces

SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
A local neural classifier for the recognition of EEG patterns associated to mental tasks

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Aiming at the recognition problem of EEG signals in brain-computer interfaces (BCIs), we present a pattern recognition method. The method combines an adaptive genetic algorithm (GA) with the support vector machine (SVM). It integrates the following three key techniques: (1) the feature selection and model parameters of the SVM are optimized synchronously, which constitutes a hybrid optimization; (2) the aim of the hybrid optimization is to improve the classification performance of the SVM; and (3) the hybrid optimization is solved by using the adaptive GA. The method is used to classify three types of EEG signals produced during motor imaginations. It yields 72% classification accuracy, which is higher 8% than the one obtained with the individual optimization of the feature selection and SVM parameters.