Published April 2011
| public
Journal Article
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.
Additional Information
© 2010 Biomedical Engineering Society. Received 1 September 2010; accepted 3 November 2010; published online 18 November 2010. Associate Editor Angelique Louie oversaw the review of this article. The authors acknowledge the founding provided by the USA National Science Foundation through BMES-ERC and UC Lab Fee Program. The authors acknowledge the start-up grant provided by National University of Singapore. The authors are grateful to Dr. Victor Pikov, Eric Basham, Plexon, and BMESERC Cortical Testbed for providing in vivo neural data and suggestions. The authors acknowledge the in vivo database contributed by Gyorgy Buzsáki lab (http:// crcns.org/data-sets/hc/) and synthesized database contributed by Quian Quiroga (http://www.vis.caltech. edu/~rodri/Wave_clus/Wave_clus_home.htm).Additional details
- Eprint ID
- 23459
- Resolver ID
- CaltechAUTHORS:20110426-075623090
- National University of Singapore
- NSF
- University of California
- Created
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2011-05-05Created from EPrint's datestamp field
- Updated
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2021-11-09Created from EPrint's last_modified field