High performance spike detection and sorting using neural waveform phase information and SOM clustering

Neural spike detection is the very first step in the analysis of recorded neural waveforms for brain machine interface applications and for neuroscientific studies. Spike detection accuracy and algorithm robustness is an important consideration in developing detection algorithms. For real neural recording data without respective ground truth, the evaluation of detection performance is a challenge. In the present paper we evaluate the detections by inspecting the detected spike waveforms for their compliance with neural spike electrophysiological properties. After classifying similar waveforms into one cluster, those qualified detections are determined to be spikes with high confidence. This new spike detection evaluation method is based on using the waveform phase information for cluster analysis. By including clustering as an integral step in the detection algorithm, we can refine detection results and improve detection performance. The new algorithm is easy to implement and is effective as demonstrated using both artificial and real neural waveforms.