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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Rizk, M; Wolf, PD

Published Date

  • September 2009

Published In

Volume / Issue

  • 47 / 9

Start / End Page

  • 955 - 966

PubMed ID

  • 19205769

Pubmed Central ID

  • 19205769

Electronic International Standard Serial Number (EISSN)

  • 1741-0444

International Standard Serial Number (ISSN)

  • 0140-0118

Digital Object Identifier (DOI)

  • 10.1007/s11517-009-0451-2

Language

  • eng