Spatial Information Based OSort for Real-Time Spike Sorting Using FPGA

• Published in: IEEE transactions on biomedical engineering Vol. 68; no. 1; pp. 99 - 108
• Main Authors: Schaffer, Laszlo, Nagy, Zoltan, Kincses, Zoltan, Fiath, Richard, Ulbert, Istvan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Arrays; Clustering; Computers; Electrodes; Field programmable gate arrays; Firing pattern; FPGA; In vivo methods and tests; Information processing; multi-channel; Neurons; Noise levels; online spike sorting; Probes; Real time; Real-time systems; Recording; Signal to noise ratio; Sorting; Sorting algorithms; Spatial data; spatial information; Spikes; Template matching; Unit activity
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2020.2996281

Objective: Spiking activity of individual neurons can be separated from the acquired multi-unit activity with spike sorting methods. Processing the recorded high-dimensional neural data can take a large amount of time when performed on general-purpose computers. Methods: In this paper, an FPGA-based real-time spike sorting system is presented which takes into account the spatial correlation between the electrical signals recorded with closely-packed recording sites to cluster multi-channel neural data. The system uses a spatial window-based version of the Online Sorting algorithm, which uses unsupervised template-matching for clustering. Results: The test results show that the proposed system can reach an average accuracy of 86% using simulated data (16-32 neurons, 4-10 dB Signal-to-Noise Ratio), while the single-channel clustering version achieves only 74% average accuracy in the same cases on a 128-channel electrode array. The developed system was also tested on in vivo cortical recordings obtained from an anesthetized rat. Conclusion: The proposed FPGA-based spike sorting system can process more than 11000 spikes/second, so it can be used during in vivo experiments providing real-time feedback on the location and electrophysiological properties of well-separable single units. Significance: The proposed spike sorting system could be used to reduce the positioning error of the closely-packed recording site during a neural measurement.