Automatic Spike Sorting For Real-time Applications

Abstract

Real-time applications of spike sorting, e.g., neural decoding, generally require high numbers of channels, and manual spike sorting methods are extremely time consuming, subjective and, generally, do not perform well for low signal-to-noise ratio (SNR) signals. Hence, an automatic method is sought which is efficient and robust in both detecting neural spikes and constructing a classification model of spikes arriving with underlying statistics that are time-varying. We present such a system under study for application with a microelectrode array of 96 channels with typically three or four units (Le., neurons) per channel. There are several novel elements of the system including filtering the neural signal to a frequency band having better SNR for spike detection, a fixed feature space for simple implementation yet adequate resolving capabilities, a Gaussian statistics model also for simple implementation as a log-likelihood classifier, a systematic approach to determining the number of clusters in a pattern recognition problem, and a robust linear discriminant, histogram-based technique for determining boundaries between feature space clusters