基于局部保持投影的神经尖峰电位特征提取与分类

Unsupervised spike extraction and classification based on locality preserving projection

神经元尖峰电位的识别和分类,是神经信息处理中的关键环节之一,而尖峰电位的特征提取是识别和分类的重要基础。针对尖峰电位的特征提取和分类,提出一种基于局部保持投影(LPP)的无监督算法,对近邻参数进行了自动识别和选择,使用基于原型向量的分布离散度标准,尖峰电位的特征得到充分提取和分离。仿真和实际数据实验结果表明:基于局部保持投影的无监督特征提取和分类算法,比传统主成分分析(PCA)方法能更加有效地实现特征提取和分离。

The spike sorting, including neuronal spike waveform acquisition and classification, is one of the important procedures in neuronal information processing, and its feature extraction and recognition are the basis of the above issues. Based on Locality Preserving Projection （ LPP） algorithm, an unsupervised feature extraction and classification algorithm was proposed. The neighbor parameter was selected automatically, the distribution dispersion standard was obtained according to the original data set, and the features of extraction results in spikes were separated effectively. The application in simulation and real experimental data show that, the proposed method based on the LPP can more effectively extract and separate features of spikes in comparison of the traditional Principle Component Analysis （ PCA） algorithm.