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

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Abstract

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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Acknowledgments

We would like to thank Dr. Iyad Obeid for his helpful comments. This work was supported by Grant Number F31EB007897 from the National Institute of Biomedical Imaging and Bioengineering.

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  1. Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA

    Michael Rizk & Patrick D. Wolf

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  1. Michael Rizk
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  2. Patrick D. Wolf
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Correspondence to Michael Rizk.

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Rizk, M., Wolf, P.D. Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level. Med Biol Eng Comput 47, 955–966 (2009). https://doi.org/10.1007/s11517-009-0451-2

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  • DOI: https://doi.org/10.1007/s11517-009-0451-2

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