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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.

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Title Unified selective sorting approach to analyse multi-electrode extracellular data
Author(s) Veerabhadrappa, R.
Lim, C.P.ORCID iD for Lim, C.P. orcid.org/0000-0003-4191-9083
Nguyen, T.T.ORCID iD for Nguyen, T.T. orcid.org/0000-0001-9709-1663
Berk, M.ORCID iD for Berk, M. orcid.org/0000-0002-5554-6946
Tye, S.J.
Monaghan, P.
Nahavandi, S.ORCID iD for Nahavandi, S. orcid.org/0000-0002-0360-5270
Bhatti, A.ORCID iD for 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 Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084580

Document type: Journal Article
Collections: Faculty of Health
School of Medicine
Open Access Collection
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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.