Please use this identifier to cite or link to this item: https://doi.org/10.1088/1741-2560/9/4/046017
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dc.titleA new EC-PC threshold estimation method for in vivo neural spike detection
dc.contributor.authorYang, Z.
dc.contributor.authorLiu, W.
dc.contributor.authorKeshtkaran, M.R.
dc.contributor.authorZhou, Y.
dc.contributor.authorXu, J.
dc.contributor.authorPikov, V.
dc.contributor.authorGuan, C.
dc.contributor.authorLian, Y.
dc.date.accessioned2014-06-16T09:31:55Z
dc.date.available2014-06-16T09:31:55Z
dc.date.issued2012-08
dc.identifier.citationYang, 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/10.1088/1741-2560/9/4/046017
dc.identifier.issn17412560
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54511
dc.description.abstractThis 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.
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1088/1741-2560/9/4/046017
dc.description.sourcetitleJournal of Neural Engineering
dc.description.volume9
dc.description.issue4
dc.description.page-
dc.identifier.isiut000306759600030
Appears in Collections:Staff Publications

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