Ghanbari Yasser, Spence Larry, Papamichalis Panos
Department of Electrical Engineering, Southern Methodist University, Dallas, TX 75275, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3142-5. doi: 10.1109/IEMBS.2009.5332571.
Analysis of extracellular neural spike recordings is highly dependent upon the accuracy of neural waveform classification, commonly referred to as spike sorting. Feature extraction is an important stage of this process because it can limit the quality of clustering which is performed in the feature space. This paper proposes a new feature extraction method (which we call Graph Laplacian Features, GLF) based on minimizing the graph Laplacian and maximizing the weighted variance. The algorithm is compared with Principal Components Analysis (PCA, the most commonly-used feature extraction method) using simulated neural data. The results show that the proposed algorithm produces more compact and well-separated clusters compared to PCA. As an added benefit, tentative cluster centers are output which can be used to initialize a subsequent clustering stage.
细胞外神经尖峰记录的分析高度依赖于神经波形分类的准确性，通常称为尖峰排序。特征提取是这个过程的一个重要阶段，因为它会限制在特征空间中进行的聚类质量。本文提出了一种基于最小化图拉普拉斯算子和最大化加权方差的新特征提取方法（我们称之为图拉普拉斯特征，GLF）。使用模拟神经数据将该算法与主成分分析（PCA，最常用的特征提取方法）进行了比较。结果表明，与PCA相比，所提出的算法产生的聚类更紧凑且分离良好。另外一个好处是，会输出暂定的聚类中心，可用于初始化后续的聚类阶段。
