학술논문
A data-driven regularization approach for template matching in spike sorting with high-density neural probes
Document Type
Conference
Source
2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Engineering in Medicine and Biology Society (EMBC), 2019 41st Annual International Conference of the IEEE. :4376-4379 Jul, 2019
Subject
Language
ISSN
1558-4615
Abstract
Spike sorting is the process of assigning neural spikes in an extracellular brain recording to their putative neurons. Optimal pre-whitened template matching filters that are used in spike sorting typically suffer from ill-conditioning. In this paper, we investigate the origin of this ill-conditioning and the way in which it influences the resulting filters. Two data-driven subspace regularization approaches are proposed, and those are shown to outperform a regularization approach used in recent literature. The comparison of the methods is based on ground truth data that are recorded in-vivo.