Skip to main content
Journal cover image

Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level.

Publication ,  Journal Article
Rizk, M; Wolf, PD
Published in: Medical & biological engineering & computing
September 2009

Thresholding is an often-used method of spike detection for implantable neural signal processors due to its computational simplicity. A means for automatically selecting the threshold is desirable, especially for high channel count data acquisition systems. Estimating the noise level and setting the threshold to a multiple of this level is a computationally simple means of automatically selecting a threshold. We present an analysis of this method as it is commonly applied to neural waveforms. Four different operators were used to estimate the noise level in neural waveforms and set thresholds for spike detection. An optimal multiplier was identified for each noise measure using a metric appropriate for a brain-machine interface application. The commonly used root-mean-square operator was found to be least advantageous for setting the threshold. Investigators using this form of automatic threshold selection or developing new unsupervised methods can benefit from the optimization framework presented here.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Medical & biological engineering & computing

DOI

EISSN

1741-0444

ISSN

0140-0118

Publication Date

September 2009

Volume

47

Issue

9

Start / End Page

955 / 966

Related Subject Headings

  • User-Computer Interface
  • Signal Processing, Computer-Assisted
  • Neurons
  • Man-Machine Systems
  • Humans
  • Electrodes, Implanted
  • Electricity
  • Brain
  • Biomedical Engineering
  • 4611 Machine learning
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Rizk, M., & Wolf, P. D. (2009). Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level. Medical & Biological Engineering & Computing, 47(9), 955–966. https://doi.org/10.1007/s11517-009-0451-2
Rizk, Michael, and Patrick D. Wolf. “Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level.Medical & Biological Engineering & Computing 47, no. 9 (September 2009): 955–66. https://doi.org/10.1007/s11517-009-0451-2.
Rizk M, Wolf PD. Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level. Medical & biological engineering & computing. 2009 Sep;47(9):955–66.
Rizk, Michael, and Patrick D. Wolf. “Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level.Medical & Biological Engineering & Computing, vol. 47, no. 9, Sept. 2009, pp. 955–66. Epmc, doi:10.1007/s11517-009-0451-2.
Rizk M, Wolf PD. Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level. Medical & biological engineering & computing. 2009 Sep;47(9):955–966.
Journal cover image

Published In

Medical & biological engineering & computing

DOI

EISSN

1741-0444

ISSN

0140-0118

Publication Date

September 2009

Volume

47

Issue

9

Start / End Page

955 / 966

Related Subject Headings

  • User-Computer Interface
  • Signal Processing, Computer-Assisted
  • Neurons
  • Man-Machine Systems
  • Humans
  • Electrodes, Implanted
  • Electricity
  • Brain
  • Biomedical Engineering
  • 4611 Machine learning