Please use this identifier to cite or link to this item: https://doi.org/4/046017
Title: A new EC-PC threshold estimation method for in vivo neural spike detection
Authors: Yang, Z. 
Liu, W.
Keshtkaran, M.R.
Zhou, Y.
Xu, J. 
Pikov, V.
Guan, C.
Lian, Y. 
Issue Date: Aug-2012
Source: Yang, Z.,Liu, W.,Keshtkaran, M.R.,Zhou, Y.,Xu, J.,Pikov, V.,Guan, C.,Lian, Y. (2012-08). A new EC-PC threshold estimation method for in vivo neural spike detection. Journal of Neural Engineering 9 (4) : -. ScholarBank@NUS Repository. https://doi.org/4/046017
Abstract: This paper models in vivo neural signals and noise for extracellular spike detection. Although the recorded data approximately follow Gaussian distribution, they clearly deviate from white Gaussian noise due to neuronal synchronization and sparse distribution of spike energy. Our study predicts the coexistence of two components embedded in neural data dynamics, one in the exponential form (noise) and the other in the power form (neural spikes). The prediction of the two components has been confirmed in experiments of in vivo sequences recorded from the hippocampus, cortex surface, and spinal cord; both acute and long-term recordings; and sleep and awake states. These two components are further used as references for threshold estimation. Different from the conventional wisdom of setting a threshold at 3xRMS, the estimated threshold exhibits a significant variation. When our algorithm was tested on synthesized sequences with a different signal to noise ratio and on/off firing dynamics, inferred threshold statistics track the benchmarks well. We envision that this work may be applied to a wide range of experiments as a front-end data analysis tool. © 2012 IOP Publishing Ltd.
Source Title: Journal of Neural Engineering
URI: http://scholarbank.nus.edu.sg/handle/10635/54511
ISSN: 17412560
DOI: 4/046017
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

42
checked on Dec 8, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.