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Computational Intelligence and Neuroscience
Volume 2007 (2007), Article ID 94397, 9 pages
http://dx.doi.org/10.1155/2007/94397
Research Article

A Semisupervised Support Vector Machines Algorithm for BCI Systems

1Institute of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China
2Shenzhen Institute of Advanced Integration Technology, Chinese Academy of Sciences, The Chinese University of Hong Kong, Hong Kong
3School of Maths, Central-South University, Changsha 410008, China

Received 30 January 2007; Accepted 18 June 2007

Academic Editor: Andrzej Cichocki

Copyright © 2007 Jianzhao Qin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm.