title: Comparison of spike-sorting algorithms for future hardware implementation.
abstract: Applications such as brain-machine interfaces require hardware spike sorting in order to (1) obtain single-unit activity and (2) perform data reduction for wireless transmission of data. Such systems must be low-power, low-area, high-accuracy, automatic, and able to operate in real time. Several detection and feature extraction algorithms for spike sorting are described briefly and evaluated in terms of accuracy versus computational complexity. The nonlinear energy operator method is chosen as the optimal spike detection algorithm, being most robust over noise and relatively simple. The discrete derivatives method [1] is chosen as the optimal feature extraction method, maintaining high accuracy across SNRs with a complexity orders of magnitude less than that of traditional methods such as PCA.
authors.affiliation: Department of Electrical Engineering, University of California, Los Angeles, USA. sarah@ee.ucla.edu
authors.forename: Sarah
authors.initials: S
authors.lastname: Gibson
authors.forename: Jack W
authors.initials: JW
authors.lastname: Judy
authors.forename: Dejan
authors.initials: D
authors.lastname: Markovic
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*