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An unsupervised real-time spike sorting system based on optimized OSort.

Yingjiang Wu^1,2,3, Ben-Zheng Li^4,5, Liyang Wang^6,7

¹School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China.

Journal of Neural Engineering

|November 16, 2023

Summary

This summary is machine-generated.

An optimized spike sorting algorithm (opt-OSort) significantly reduces transient clusters and memory usage by using correlation coefficients for robust classification. This rapid, precise solution is ideal for real-time neural interfaces.

Keywords:

OSort electrode drifting hardware implementation low-power electronics spike sorting

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Area of Science:

Computational Neuroscience
Hardware Implementation of Neural Signal Processing
Biomedical Engineering

Background:

The OSort algorithm is a key unsupervised method for real-time spike sorting in neural recordings.
Existing hardware implementations of OSort face challenges with transient clusters, leading to high memory and computational demands, especially in noisy, multi-channel environments.

Purpose of the Study:

To introduce an optimized OSort algorithm (opt-OSort) for improved real-time spike sorting.
To enhance robustness and reduce resource requirements compared to existing hardware implementations.

Main Methods:

Developed opt-OSort utilizing the correlation coefficient (CC) as the primary classification criterion, replacing Euclidean distance.
Integrated two configurable validation loops for real-time outlier rejection and tracking of electrode drift.
Evaluated performance on simulated and experimental neural data using hardware implementation.

Main Results:

opt-OSort reduced transient cluster occurrences by two orders of magnitude and memory usage by 2.5-80 times.
Achieved sorting accuracy comparable to offline OSort and other algorithms, with a hardware sorting time of 0.68 µs.
Demonstrated effective handling of neural activity variations due to electrode drifting.

Conclusions:

The opt-OSort algorithm provides a significant advancement in spike sorting efficiency and robustness.
This optimized solution is well-suited for integration into low-power, portable neural control systems and brain-computer interfaces.
The CC-based approach offers a reliable method for spike classification in challenging recording conditions.