EEG classification based on probabilistic neural network with supervised learning in brain computer interface

吴婷; Yan Guozheng; Yang Banghua; Sun Hong

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EEG classification based on probabilistic neural network with supervised learning in brain computer interface

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Aiming at the topic of electroencephalogram (EEG) pattern recognition in brain computer interface(BCI), a classification method based on probabilistic neural network (PNN) with supervised learning ispresented in this paper. It applies the recognition rate of training samples to the learning progress of networkparameters. The learning vector quantization is employed to group training samples and the Geneticalgorithm (GA) is used for training the network's smoothing parameters and hidden central vector for determininghidden neurons. Utilizing the standard dataset Ⅰ(a) of BCI Competition 2003 and comparingwith other classification methods, the experiment results show that the best performance of pattern recognitionis got in this way, and the classification accuracy can reach to 93.8 % , which improves over 5 %compared with the best result (88.7 %) of the competition. This technology provides an effective way toEEG classification in practical system of BCI.

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《高技术通讯：英文版》 |2009年第4期|384-387|共4页
作者
吴婷; Yan Guozheng; Yang Banghua; Sun Hong;
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作者单位

School of Electronic;

Information and Electrical Engineering;

Shanghai Jiaotong University;

Shanghai 200240;

P.R. China;

School of Mechanical Engineering;

Shanghai Dian Ji University;

Shanghai 200240;

P. R. China;

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原文格式 PDF
正文语种 chi
中图分类人工智能理论;接口装置、插件;
关键词
概率神经网络; 计算机接口; 分类方法; 监督学习; 脑电图; 学习矢量量化; 模式识别; 训练样本;

机译：概率神经网络;计算机接口;分类方法;监督学习;脑电图;学习矢量量化;模式识别;训练样本;

EEG classification based on probabilistic neural network with supervised learning in brain computer interface

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