title: Optimal detection, classification, and superposition resolution in neural waveform recordings.
abstract: The effects of noise autocorrelation on neural waveform recognition (detection, classification, and superposition resolution) are investigated in this study using microelectrode recordings from the cortex of a monkey. Optimal waveform recognition is accomplished by passing the data through a whitening filter before matched filtering for detection or template matching for classification and superposition resolution. Template matching without whitening requires about 40% higher signal-to-noise ratio than template matching with whitening for comparable classification and superposition resolution. The comparable difference for detection is 15%.
authors.affiliation: Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723.
authors.forename: I N
authors.initials: IN
authors.lastname: Bankman
authors.forename: K O
authors.initials: KO
authors.lastname: Johnson
authors.forename: W
authors.initials: W
authors.lastname: Schneider
mesh_terms: D000200:Action Potentials; D000818:Animals; D016477:Artifacts; D001499:Bayes Theorem; D004594:Electrophysiology / Q000145:classification / Q000379:methods / Q000706:statistics & numerical data; D000882:Haplorhini; D009474:Neurons / Q000502:physiology*; D013003:Somatosensory Cortex / Q000502:physiology