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Published Articles >> Table of Contents >> Abstract
19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06)
pp. 396-400
Pattern Recognition Using Hybrid Optimization for a Robot Controlled by Human Thoughts
Yan Guozheng, Shanghai Jiao Tong University, China
Yang Banghua, Shanghai Jiao Tong University, China
Chen Shuo, Zhejiang University, China
Yan Rongguo, Shanghai Jiao Tong University, China
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CBMS.2006.127
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| Abstract |
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A robot system controlled by human thoughts is
introduced in this paper. Aiming at the recognition
problem of electroencephalogram (EEG) signals in the
system, we present a novel pattern recognition method.
The method combines the genetic algorithm (GA) with
the support vector machine (SVM). It includes two
techniques. One is that the feature selection and model
parameters of the SVM are optimized synchronously,
which constitutes a hybrid optimization. The other is
that the hybrid optimization is realized by using the
GA. The method is used to classify three types of EEG
signals in the system. The experiment results show that
this method can yield significantly higher classification
accuracy than ones obtained with individual
optimizations.
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 Additional Information
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Citation:
Yan Guozheng, Yang Banghua, Chen Shuo, Yan Rongguo,
"Pattern Recognition Using Hybrid Optimization for a Robot Controlled by Human Thoughts,"
cbms,
pp. 396-400,
19th IEEE Symposium on Computer-Based Medical Systems (CBMS'06),
2006
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