An Investigation on Neural Spike Sorting Algorithms

Abstract

Spike sorting is a technique used to detect signals generated by the neurons of the brain and to classify which spike belongs to which neurons. Spike sorting is one of the most important techniques used by the electrophysiological data processing. Spike Sorting Algorithms (SSA) are created to differentiate the behavior of one or more neurons from background electric noise using waveforms from one or more electrodes in the brain. This sorting comes out as having an essential role in extracting information from extracellular recordings in the neurosciences research community. There are many steps for Spike sorting algorithm (Detection, feature extraction, and Clustering). One of the most important things in spike sorting is the accuracy of the classification for neuron spikes. This article gives a brief overview of the spike sorting algorithm, and the contribution of this paper Karzan.hussein@uhd.edu.iq a comprehensive overview of the previous works on the spike sorting Karzan.hussein@uhd.edu.iq steps Karzan.hussein@uhd.edu.iq (Detection, Feature extraction, and Clustering). The used new techniques to solve the problem of overlapping. On the other hand, previous works used real-time or online spike sorting instead of offline spike sorting. The previous researchers used machine learning algorithms for automatic classification for the spike sorting.