Unified selective sorting approach to analyse multi-electrode extracellular data Veerabhadrappa, R., Lim, C.P., Nguyen, T.T., Berk, M., Tye, S.J., Monaghan, P., Nahavandi, S. and Bhatti, A. 2016, Unified selective sorting approach to analyse multi-electrode extracellular data, Scientific reports, vol. 6, Article number : 28533, pp. 1-16, doi: 10.1038/srep28533. Attached Files Name Description MIMEType Size Downloads lim-unifiedselectivesorting-2016.pdf Published version application/pdf 2.50MB 87 Title Unified selective sorting approach to analyse multi-electrode extracellular data Author(s) Veerabhadrappa, R. Lim, C.P. orcid.org/0000-0003-4191-9083 Nguyen, T.T. orcid.org/0000-0001-9709-1663 Berk, M. orcid.org/0000-0002-5554-6946 Tye, S.J. Monaghan, P. Nahavandi, S. orcid.org/0000-0002-0360-5270 Bhatti, A. orcid.org/0000-0001-6876-1437 Journal name Scientific reports Volume number 6 Season Article number : 28533 Start page 1 End page 16 Total pages 16 Publisher Nature Publishing Group Place of publication London, Eng. Publication date 2016 ISSN 2045-2322 Summary Extracellular data analysis has become a quintessential method for understanding the neurophysiological responses to stimuli. This demands stringent techniques owing to the complicated nature of the recording environment. In this paper, we highlight the challenges in extracellular multi-electrode recording and data analysis as well as the limitations pertaining to some of the currently employed methodologies. To address some of the challenges, we present a unified algorithm in the form of selective sorting. Selective sorting is modelled around hypothesized generative model, which addresses the natural phenomena of spikes triggered by an intricate neuronal population. The algorithm incorporates Cepstrum of Bispectrum, ad hoc clustering algorithms, wavelet transforms, least square and correlation concepts which strategically tailors a sequence to characterize and form distinctive clusters. Additionally, we demonstrate the influence of noise modelled wavelets to sort overlapping spikes. The algorithm is evaluated using both raw and synthesized data sets with different levels of complexity and the performances are tabulated for comparison using widely accepted qualitative and quantitative indicators. Language eng DOI 10.1038/srep28533 Field of Research 090609 Signal Processing 110902 Cellular Nervous System 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 970110 Expanding Knowledge in Technology HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2016, Nature Publishing Group Free to Read? Yes Use Rights Persistent URL http://hdl.handle.net/10536/DRO/DU:30084580 Document type: Journal Article Collections: Faculty of Health School of Medicine Open Access Collection Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Fri, 01 Jul 2016, 11:14:55 EST Fri, 01 Jul 2016, 12:47:47 EST Fri, 01 Jul 2016, 13:03:30 EST Wed, 13 Jul 2016, 06:08:48 EST Wed, 12 Oct 2016, 07:53:27 EST Wed, 12 Oct 2016, 07:57:54 EST Wed, 12 Oct 2016, 07:58:49 EST Fri, 11 Nov 2016, 12:06:28 EST Fri, 07 Sep 2018, 12:13:14 EST Filtered Full Citation counts:   Cited 5 times in TR Web of Science Cited 5 times in Scopus Search Google Scholar Access Statistics: 547 Abstract Views, 88 File Downloads  -  Detailed Statistics Created: Fri, 01 Jul 2016, 11:14:55 EST

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