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bioRxiv · 10.1101/2022.01.18.476764

pyNeurode: a real-time neural signal processing framework

Tam, W. K.·Nolan, M. F.
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Abstract

Accurate decoding of neural signals often requires assigning extracellular waveforms acquired on the same electrode to their originating neurons, a process known as spike sorting. While many offline sorters are available, accurate online sorting of spikes with many channels is still a challenging problem. Existing online sorters either use simple algorithms with low accuracy, can only process a handful of channels, or depend on a complex runtime environment that is difficult to set up. We have developed a state-of-the-art online spike sorting platform in Python that enables large-scale, fully automatic real-time spike sorting and decoding on hundreds of channels. Our system is cross-platform and works seamlessly with the Open Ephys suite of open-source hardware and software widely used in many neuroscience laboratories worldwide. It also comes with a user-friendly graphical user interface to monitor the cluster quality, spike waveforms and neuronal firing rate. Our platform has comparable accuracy to offline sorters and can achieve an end-to-end sorting latency of around 160 ms for 128-channel signals. It will be useful for research in fundamental neuroscience, closed-loop feedback neuromodulation and brain-computer interfaces.

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Tam, W. K., Nolan, M. F.. 2022-01-20. pyNeurode: a real-time neural signal processing framework. https://doi.org/10.1101/2022.01.18.476764

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