A pulse-based feature extractor for spike sorting neural signals

Christy L. Rogers, John G. Harris, Jose C. Principe, Justin C. Sanchez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Spike sorting is often required for analyzing neural recordings to isolate the activity of single neurons. Wave shape analysis in the spike sorting procedure provides a means to detect spikes while minimizing the influence of false alarms. As neural recording techniques allow for recording hundreds of electrodes, power is too limited in neural implants for current spike sorting algorithms. Even with spike sorting at the back-end where more power is available, the bandwidth is too limited to transmit enough information for current spike sorting techniques. A low-power pulse-based feature extractor presented in the paper is a solution to the bandwidth bottleneck. It reduces the bandwidth of the neural signal by several orders of magnitude while preserving enough information for spike sorting.

Original languageEnglish (US)
Title of host publicationProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering
Pages490-493
Number of pages4
DOIs
StatePublished - Sep 25 2007
Event3rd International IEEE EMBS Conference on Neural Engineering - Kohala Coast, HI, United States
Duration: May 2 2007May 5 2007

Publication series

NameProceedings of the 3rd International IEEE EMBS Conference on Neural Engineering

Other

Other3rd International IEEE EMBS Conference on Neural Engineering
CountryUnited States
CityKohala Coast, HI
Period5/2/075/5/07

ASJC Scopus subject areas

  • Biotechnology
  • Bioengineering
  • Neuroscience (miscellaneous)

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  • Cite this

    Rogers, C. L., Harris, J. G., Principe, J. C., & Sanchez, J. C. (2007). A pulse-based feature extractor for spike sorting neural signals. In Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering (pp. 490-493). [4227321] (Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering). https://doi.org/10.1109/CNE.2007.369716