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

作　　者：吴婷 Yan Guozheng Yang Banghua Sun Hong 

机构地区：[1]School of Electronic, Information and Electrical Engineering, Shanghai Jiaotong University, Shanghai 200240, P.R. China [2]School of Mechanical Engineering, Shanghai Dian Ji University, Shanghai 200240, P. R. China

出　　处：《High Technology Letters》2009年第4期384-387,共4页高技术通讯（英文版）

基　　金：Supported by the National Natural Science Foundation of China (No. 30570485);the Shanghai "Chen Guang" Project (No. 09CG69).

摘　　要：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 is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js 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 to EEG classification in practical system of BCI.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.

关 键 词：Probabilistic neural network （PNN） supervised learning brain computer interface （BCI） electroencephalogram （EEG） 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]