Objective evaluation of stimulation artefact removal techniques in the context of neural spike sorting. (1st February 2022)

• Record Type: Journal Article
• Authors: Schelles, Maarten; Wouters, Jasper; Asamoah, Boateng; Mc Laughlin, Myles; Bertrand, Alexander
• Is Part Of: Journal of neural engineering. Volume 19:Number 1(2022)
• Publication Date: 2022-02-01
• Subjects: artefact removal -- neural spike sorting -- linear filter design; Neurosciences -- Periodicals; Biomedical engineering -- Periodicals; 612.8
• DOI: 10.1088/1741-2552/ac4ecf
• Languages: English
• ISSNs: 1741-2560

Abstract

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.