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

Abstract

<i>Objective</i>. We present a framework to objectively test and compare stimulation artefact removal techniques in the context of neural spike sorting.<i>Approach</i>. To this end, we used realistic hybrid ground-truth spiking data, with superimposed artefacts from<i>in vivo</i>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).<i>Main results</i>. 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<i>in vivo</i>recordings without artificial manipulations.<i>Significance</i>. 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.