A fully automatic multichannel neural spike sorting algorithm with spike reduction and positional feature

Abstract

The sorting of neural spike data recorded by multichannel and high channel neural probes such as Neuropixels, especially in real-time, remains a significant technical challenge. Most neural spike sorting algorithms focus on sorting neural spikes post-hoc for high sorting accuracy - but reducing the processing delay for fast sorting, potentially even live sorting, is generally not possible with these alogorithms. Here we report our Graph nEtwork Multichannel (GEMsort) algorithm, which is largely based on graph network, to allow rapid neural spike sorting for multiple neural recording channels. This was accomplished by two innovations: In GEMsort, duplicated neural spikes recorded from multiple channels were eliminated from duplicate channels by only selecting the highest amplitude neural spike in any channel for subsequent processing. In addition, the channel from which the representative neural spike was recorded was used as an additional feature to differentiate between neural spikes recorded from different neurons having similar temporal features. These algorithmic modifications allow GEMsort to rapidly sort neural spikes and this approach is highly suitable to be implemented with digital circuitry for high processing speed and channel scalability. Synthetic and experimentally recorded multichannel neural recordings were used to evaluate the sorting performance of GEMsort. The sorting results of GEMsort were also compared with two other state-of-the-art sorting algorithms (Kilosort and Mountainsort) in sorting time and sorting agreements.