title: Matched subspace detector based feature extraction for sorting of multi-sensor action potentials.
abstract: This paper proposes a novel matched subspace detector (MSD) based algorithm for extracting discriminant features from multi-sensor measurements of extracellular action potentials (APs) to facilitate their subsequent separation according to the neuron of origin. The method does not require the construction of AP templates, and is therefore suitable for unsupervised AP sorting applications. In addition, detailed simulations show that the proposed algorithm outperforms existing single-sensor based feature extraction approaches.
authors.affiliation: Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA. scwu@uci.edu
authors.forename: Shun Chi
authors.initials: SC
authors.lastname: Wu
authors.forename: A Lee
authors.initials: AL
authors.lastname: Swindlehurst
authors.forename: Zoran
authors.initials: Z
authors.lastname: Nenadic
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D009474:Neurons / Q000166:cytology