摘要
对神经元动作电位进行模式分类是植入式脑机接口研究的前期关键问题。考虑到来自不同神经元的动作电位在时域或频域特征上的相似性,引入小波分析在时频域上对动作电位进行特征描述。首先以db、sym、bior三类小波函数系为例,分别获取了动作电位的高维小波系数特征;然后对特征分量进行非正态分布特性的KS检验,以实现特征降维;最后通过非监督的K均值方法完成动作电位聚类。实验结果表明:在神经信号噪声水平为0.05dB、0.1dB和0.15dB时,各小波基的分类性能略有不同。其中sym5小波性能突出,动作电位错分率基本稳定在1.21%～1.81%。最后与主成分分析法(PCA)进行了分类性能的比较,进一步证实了小波时频分析方法(sym5小波)在抗干扰性和稳定性方面的优势。
The separation of spikes is a key problem for invasive brain-computer interface. To deal with the similarity of spike temporal profile and frequency feature, a method was proposed to represent spike feature using wavelet analysis technique. First, wavelet functions, such as db, sym, bior, were used as base function to achieve high-dimension wavelet coefficient as spike feature. Next, in order to decrease the dimension of spike feature, Kolm0gorov-Smirnov （KS） test was performed to select a few coefficients. After that, unsupervised K-means clustering was calculated to complete spike sorting. The experimental results show that, when the neural signal is at the noise level 0.05 dB, 0. 1 dB, 0. 15 dB, sorting performance varies slightly while changing wavelet base functions. In all of these functions, sym5 wavelet outperforms the other five wavelet functions in terms of misclassified rate of spikes （between 1. 21% ~ 1. 81%）. Compared with Principal Component Analysis （PCA）, the proposed method based on sym5 wavelet performs better even for the heavy noise spike data.
出处
《计算机应用》
CSCD
北大核心
2010年第10期2723-2726,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(30770685)
浙江省新苗人才计划项目(2007G60G2040006)
关键词
电位分类
小波时频分析
小波基函数
KS检验
spike sorting
wavelet time-frequency analysis
wavelet base function
Kolmogorov-Smirnov （KS） test
