A Five-Channel Weighted Real-Time Algorithm for High-Density Electrodes Spike Sorting.

Abstract

With the application of high-density neural probes, a neuron can be detected by multiple adjacent probes, and the traditional single-channel spike sorting is no longer suitable. In this paper, we propose a five-channel weighted real-time spike sorting algorithm based on template-matching to process neural signals recorded by high-density probes. This work uses the signals of the center channel and the adjacent four channels to form a five-channel template by weighting, and employs a modified OSort algorithm with unsupervised learning to update the template. We implemented automatic online spike sorting, and tested it with both ground truth recordings and simulated datasets. The experiments show that our algorithm utilizing the information of adjacent channels has a higher sorting accuracy than traditional single-channel spike sorting. The average sorting accuracy reaches 89%, compared to 78% for single-channel.