title: Action potential classification with dual channel intrafascicular electrodes.
abstract: Using recordings of peripheral nerve activity made with carbon fiber intrafascicular electrodes, we compared the performance of three different recording techniques (single channel, differential, and dual channel) and four different unit classification methods (linear discriminant analysis, template matching, a novel time amplitude windowing technique, and neural networks) in terms of errors in waveform classification and artifact rejection. Dual channel recording provided uniformly superior unit separability, neural networks gave the lowest classification error rates, and template matching had the best artifact rejection performance.
authors.affiliation: Department of Bioengineering, University of Utah, Salt Lake City 84112.
authors.forename: T G
authors.initials: TG
authors.lastname: McNaughton
authors.forename: K W
authors.initials: KW
authors.lastname: Horch
mesh_terms: D000200:Action Potentials; D000818:Animals; D001369:Axons / Q000502:physiology; D001499:Bayes Theorem; D002415:Cats; D016002:Discriminant Analysis; D004566:Electrodes; D004867:Equipment Design; D016014:Linear Models; D016571:Neural Networks, Computer; D010525:Peripheral Nerves / Q000502:physiology*; D011826:Radial Nerve / Q000502:physiology; D012815:Signal Processing, Computer-Assisted