基于小波分析和SVM的P300脑电信号识别算法研究
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摘要
为了满足瘫痪人士和虚拟现实的需求,提出基于小波分析和SVM的P300脑电信号处理算法研究,并通过实验数据论证算法的可行性。本算法首先使用工频陷波器和小波分析去噪,然后使用小波分解和teager能量算子分别提取时域特征量和能量特征量,并基于SVM判断特征量是否含有P300脑电信号。实验数据表明,本算法比单一特征量判别算法有较好的判别精度,符合需求标准。
In order to meet the requirements of the paralyzed clinical and virtual reality,proposed based on wavelet analysis and SVM of P300 EEG signals processing algorithm research,and through the experimental data prove the feasibility of this algorithm. The algorithm first makes power frequency filter and wavelet denoising,and then use the wavelet decomposition and teager energy operator respectively extract the time domain characteristics and energy characteristics,and based on the SVM judge whether characteristics contain P300 EEG signals. The experimental data show that this algorithm is better than the single feature discriminant algorithm,accord with the standard requirements
In order to meet the requirements of the paralyzed clinical and virtual reality,proposed based on wavelet analysis and SVM of P300 EEG signals processing algorithm research,and through the experimental data prove the feasibility of this algorithm. The algorithm first makes power frequency filter and wavelet denoising,and then use the wavelet decomposition and teager energy operator respectively extract the time domain characteristics and energy characteristics,and based on the SVM judge whether characteristics contain P300 EEG signals. The experimental data show that this algorithm is better than the single feature discriminant algorithm,accord with the standard requirements
引文
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[4]孙中钱.基于P300和运动想象的脑机接口研究[D].济南:山东大学,2013.
[5]Koo B,Nam Y,Choi S.A hybrid EOG-P300 BCI with dual monitors[C]//Brain-Computer Interface(BCI),2014 International Winter Workshop on.IEEE,2014:1-4.
[6]苏煜.基于SCF范式的在线P300脑机接口研究[D].杭州:浙江大学,2010.
[7]施锦河.运动想象脑电信号处理与P300刺激范式研究[D].杭州:浙江大学,2012.
[8]计瑜.基于独立分量分析的P300脑电信号处理算法研究[D].杭州:浙江大学,2013.
[9]Lee W L,Tan T,Leung Y H.An improved P300extraction using ICA-R for P300-BCI speller.[C]//Conference:International Conference of the IEEE Engineering in Medicine&Biology Society IEEE Engineering in Medicine&Biology Society Conference.Conf Proc IEEE Eng Med Biol Soc,2013:7064-7067.
[10]Tayeb S,Mahmoudi A,Regragui F,et al.Efficient detection of P300 using Kernel PCA and support vector machine[C]//Complex Systems(WCCS),2014 Second World Conference on.IEEE,2014.
[11]傅伟,张新友,熊平.WICA算法在脑电P300波成分提取中的应用研究[J].医疗卫生装备,2009,30(1):4-6.
[12]王金甲,杨成杰.P300脑机接口控制智能家居系统研究[J].生物医学工程学杂志,2014(4):762-766.
[13]梁健威.基于多域融合与遗传算法的P300测谎研究[D].杭州:浙江大学,2015.
[14]张玉霞.基于P300和SSVEP的混合型脑机接口的分析与研究[D].济南:山东大学,2015.
[15]Akman Aydin E,Bay O F,Guler I.Classification of P300 event related potentials with Discrete Wavelet Transform[C]//Signal Processing and Communications Applications Conference(SIU),2015 23th.IEEE,2015.
[16]沈之芳.基于P300的脑机接口及其在线半监督学习[D].广州:华南理工大学,2014.
[17]张德丰.MATLAB小波分析[M].北京:机械工业出版社,2009.
[18]姚红革,杜亚勤,刘洋.基于小波分析和BP神经网络的图像特征提取[J].西安工业大学学报,2008(6):568-572.
[19]白宇辰,尹华超,刘三军.基于小波分析的改进KDJ指标[J].电子科技,2013(8):13-15.