Ensemble recording of multiple single-unit activity has been used to study the mechanisms of neural population coding over prolonged periods of time, and to perform reliable neural decoding in neuroprosthetic motor control applications. However, there are still many challenges towards achieving reliable stable single-units recordings. One primary challenge is the variability in spike waveform features and firing characteristics of single units recorded using chronically implanted microelectrodes, making it challenging to ascertain the identity of the recorded neurons across days. In this study, I present a fast and efficient algorithm that tracks multiple single-units recorded in non-human primates performing brain control of a robotic limb, based on features extracted from units' average waveforms and interspike intervals histograms. The algorithm requires a relatively short recording duration to perform the analysis and can be applied at the start of each recording session without requiring the subject to be engaged in a behavioral task. The algorithm achieves a classification accuracy of up to 90% compared to manual tracking. I also explore using the algorithm to develop an automated technique for unit selection to perform reliable decoding of movement parameters from neural activity.
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