Objective evaluation of stimulation artefact removal techniques in the context of neural spike sorting

Maarten Schelles, Jasper Wouters, Boateng Asamoah, Myles Mc Laughlin and Alexander Bertrand

Published 9 February 2022 • © 2022 IOP Publishing LtdJournal of Neural Engineering, Volume 19, Number 1Citation Maarten Schelles et al 2022 J. Neural Eng. 19 016020DOI 10.1088/1741-2552/ac4ecf

Abstract

Objective. We present a framework to objectively test and compare stimulation artefact removal techniques in the context of neural spike sorting. Approach. To this end, we used realistic hybrid ground-truth spiking data, with superimposed artefacts from in vivo recordings. We used the framework to evaluate and compare several techniques: blanking, template subtraction by averaging, linear regression, and a multi-channel Wiener filter (MWF). Main results. Our study demonstrates that blanking and template subtraction result in a poorer spike sorting performance than linear regression and MWF, while the latter two perform similarly. Finally, to validate the conclusions found from the hybrid evaluation framework, we also performed a qualitative analysis on in vivo recordings without artificial manipulations. Significance. Our framework allows direct quantification of the impact of the residual artefact on the spike sorting accuracy, thereby allowing for a more objective and more relevant comparison compared to indirect signal quality metrics that are estimated from the signal statistics. Furthermore, the availability of a ground truth in the form of single-unit spiking activity also facilitates a better estimation of such signal quality metrics.

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