Department of Electrical Engineering, Southern Methodist University, Dallas, TX 75275, USA.
IEEE Trans Biomed Eng. 2011 May;58(5):1365-72. doi: 10.1109/TBME.2010.2090349. Epub 2010 Nov 1.
Analysis of extracellular recordings of neural action potentials (known as spikes) 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 that is performed in the feature space. Principal components analysis (PCA) is the most commonly used feature extraction method employed for neural spike recordings. To improve upon PCA's feature extraction performance for neural spike sorting, we revisit the PCA procedure to analyze its weaknesses and describe an improved feature extraction method. This paper proposes a linear feature extraction technique that we call graph-Laplacian features, which simultaneously minimizes the graph Laplacian and maximizes variance. The algorithm's performance is compared with PCA and a wavelet-coefficient-based feature extraction algorithm on simulated single-electrode neural data. A cluster-quality metric is proposed to quantitatively measure the algorithm performance. The results show that the proposed algorithm produces more compact and well-separated clusters compared to the other approaches.
分析神经动作电位（称为尖峰）的细胞外记录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，因为它可以限制在特征空间中执行的聚类的质量。主成分分析（PCA）是最常用于神经尖峰记录的特征提取方法。为了提高 PCA 在神经尖峰分类中的特征提取性能，我们重新审视 PCA 过程，分析其弱点，并描述一种改进的特征提取方法。本文提出了一种线性特征提取技术，我们称之为图拉普拉斯特征，它同时最小化图拉普拉斯和最大化方差。在模拟单电极神经数据上，将该算法的性能与 PCA 和基于小波系数的特征提取算法进行了比较。提出了一种聚类质量度量来定量衡量算法性能。结果表明，与其他方法相比，所提出的算法产生了更紧凑和更好分离的聚类。
