title: Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier.
abstract: We report a result on neural spike sorting under conditions where the signal-to-noise ratio is very low. The use of nonlinear energy operator enables the detection of an action potential, even when the SNR is so poor that a typical amplitude thresholding method cannot be applied. The superior detection ability facilitates the collection of a training set under lower SNR than that of the methods which employ simple amplitude thresholding. Thus, the statistical characteristics of the input vectors can be better represented in the neural-network classifier. The trained neural-network classifiers yield the correct classification ratio higher than 90% when the SNR is as low as 1.2 (0.8 dB) when applied to data obtained from extracellular recording from Aplysia abdominal ganglia using a semiconductor microelectrode array.
authors.affiliation: School of Electrical Engineering, Seoul National University, Korea. khkim@helios.snu.ac.kr
authors.forename: K H
authors.initials: KH
authors.lastname: Kim
authors.forename: S J
authors.initials: SJ
authors.lastname: Kim
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001048:Aplysia / Q000502:physiology*; D009415:Nerve Net; D009431:Neural Conduction / Q000502:physiology; D012815:Signal Processing, Computer-Assisted