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

Publisher: Springer Country of Publication: United States NLM ID: 7704869 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1741-0444 (Electronic) Linking ISSN: 01400118 NLM ISO Abbreviation: Med Biol Eng Comput Subsets: MEDLINE

Publication: New York, NY : Springer
Original Publication: Stevenage, Eng., Peregrinus.

Signal Processing, Computer-Assisted* ; User-Computer Interface*; Brain/*physiology ; Neurons/*physiology; Electricity ; Electrodes, Implanted ; Humans ; Man-Machine Systems

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.

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F31 EB007897 United States EB NIBIB NIH HHS; F31 EB007897-02 United States EB NIBIB NIH HHS; F31EB007897 United States EB NIBIB NIH HHS

Date Created: 20090212 Date Completed: 20110513 Latest Revision: 20240213

20240213

PMC2734874

10.1007/s11517-009-0451-2

19205769