Detection of neuronal spikes using an adaptive threshold based on the max–min spread sorting method

Chan, Hsiao‐Lung; Lin, Ming-An; Wu, Tony; Lee, Shih‐Tseng; Tsai, Yu-Tai; Chao, Pei-Kuang

doi:10.1016/j.jneumeth.2008.04.014

Cited by 34 publications

(26 citation statements)

References 13 publications

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“…On-line unsupervised detection of spikes with high detection accuracy and low power consumption is a key area of research [1], [3]- [5]. Harrison [3] proposed the percentile operator for adaptive spike threshold detection.…”

Section: Related Workmentioning

confidence: 99%

“…Their simulation is based on synthesized neural signal with firing rate between 5 to 50 Hz, which does not cover the entire signal range of interest observed in real recordings as discussed below. More accurate but also more complex methods were also proposed such as the maxmin spread sorting operator proposed by Hsiao-Lung [1], and cap-filtering proposed by Pramodsingh et al [5].…”

Section: Related Workmentioning

confidence: 99%

“…Hsiao-Lung [1] presented an adaptive threshold method based on max-min spread sorting. However, this method has high complexity, as shown by the authors that their method requires 7 times more calculations as required by the RMS.…”

Section: Related Workmentioning

confidence: 99%

“…Here, we argue that due to their feed-forward nature, traditional methods such as P84, RMS and MD [1] by themselves are not adequate for noise level estimation and spike detection for neurological signals with high firing rate as well as neurological signals with rapid changes in the firing rate. Since for high firing rate, spikes can constitute a non-negligible portion of the recording thus including them N. S. Artan (phone: 718-260-3496; e-mail: artan@poly.edu), X. Xu (email: xxu11@students.poly.edu), and H. J. Chao (e-mail: chao@poly.edu) are with the Department of Electrical and Computer Engineering at the Polytechnic Institute of New York University, Brooklyn, NY 11201 USA.…”

Section: Introductionmentioning

confidence: 97%

“…Many researchers have investigated on-line unsupervised spike detection algorithms with high detection accuracy and low power consumption [1]. These algorithms usually perform well for high Signal-to-Noise Ratio (SNR) and low firing rate (FR), however, their performance deteriorates for low SNR and/or high FR.…”

Section: Introductionmentioning

confidence: 99%

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A low-cost reliable online noise level estimation for accurate spike detection in extracellular recordings

Artan

Chao

2012

2012 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Abstract-On-line unsupervised spike detection is a fundamental building block for autonomous neural implants. To accurately monitor neurological activity, the spike detectors should be able to operate at a wide range of firing rates. This can only be achieved by a careful estimation of instantaneous noise level in the recording. In this paper, we propose a system for estimating noise level in extracellular recordings, which is reliable across a wide range of firing rates, and thus leads to accurate spike detection.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

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A low-cost reliable online noise level estimation for accurate spike detection in extracellular recordings

Artan

Chao

2012

2012 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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Theoretical study and optimisation of a standard deviation estimator circuit for adaptive threshold spike detection

Rummens

Ygorra

Boussamba

et al. 2016

Circuit Theory & Apps

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International audienceThis paper presents a theoretical study and the resulting architecture of an analogue-based standard deviation (SD) estimator, typically used in biomedical spike detectors to assess the noise level of bioelectrical recordings online. This well-known circuit generated a significant inaccuracy, so the aim was to increase the efficiency of spike detection through proper calculation of the noise SD, by developing a rigorous, original theoretical study. The approach consisted of establishing behavioural models for the SD estimation circuit to obtain a transfer function and compare different controllers. This modelling approach also highlighted the parameters available and the impact of their relationships on design optimisation. The behavioural models, inherently based on approximations, were then initially validated by comparing them with actual circuit behaviour, obtained using discrete components. These comparisons revealed that the models matched the actual circuit measurements. Finally, taking into account the conclusions of our theoretical study, an integrated version of this adaptive threshold spike detector was designed using 0.35-μm complementary metal–oxide–semiconductor technology. Simulations of this circuit revealed that the proposed controller eliminated the static error and ensured efficient spike detection

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Detection of Intracranial Electroencephalogram Spikes by Phase Space Reconstruction

et al. 2011

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Detection of neuronal spikes using an adaptive threshold based on the max–min spread sorting method

Cited by 34 publications

References 13 publications

A low-cost reliable online noise level estimation for accurate spike detection in extracellular recordings

A low-cost reliable online noise level estimation for accurate spike detection in extracellular recordings

Theoretical study and optimisation of a standard deviation estimator circuit for adaptive threshold spike detection

Detection of Intracranial Electroencephalogram Spikes by Phase Space Reconstruction

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