Unsupervised spike extraction and classification based on locality preserving projection

Abstract

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.