title: 1/f neural noise reduction and spike feature extraction using a subset of informative samples.
abstract: This article describes a study on neural noise and neural signal feature extraction, targeting real-time spike sorting with miniaturized microchip implementation. Neuronal signature, noise shaping, and adaptive bandpass filtering are reported as the techniques to enhance the signal-to-noise ratio (SNR). A subset of informative samples of the waveforms is extracted as features for classification. Quantitative and comparative experiments with both synthesized and animal data are included to evaluate different feature extraction approaches. In addition, a preliminary hardware implementation has been realized using an integrated circuit.
authors.affiliation: Department of Electrical Engineering, University of California at Santa Cruz, CA, USA. eleyangz@nus.edu.sg
authors.forename: Zhi
authors.initials: Z
authors.lastname: Yang
authors.forename: Linh
authors.initials: L
authors.lastname: Hoang
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhao
authors.forename: Edward
authors.initials: E
authors.lastname: Keefer
authors.forename: Wentai
authors.initials: W
authors.lastname: Liu
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D001698:Biomedical Engineering; D002415:Cats; D002540:Cerebral Cortex / Q000502:physiology; D004582:Electronics, Medical / Q000295:instrumentation; D000882:Haplorhini; D006624:Hippocampus / Q000502:physiology; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D025341:Principal Component Analysis; D051381:Rats; D012815:Signal Processing, Computer-Assisted