Unified selective sorting approach to analyse multi-electrode extracellular data
Web of Science
AuthorVeerabhadrappa, R; Lim, CP; Nguyen, TT; Berk, M; Tye, SJ; Monaghan, P; Nahavandi, S; Bhatti, A
Source TitleScientific Reports
PublisherNATURE PUBLISHING GROUP
University of Melbourne Author/sBerk, Michael
Document TypeJournal Article
CitationsVeerabhadrappa, R., Lim, C. P., Nguyen, T. T., Berk, M., Tye, S. J., Monaghan, P., Nahavandi, S. & Bhatti, A. (2016). Unified selective sorting approach to analyse multi-electrode extracellular data. SCIENTIFIC REPORTS, 6 (1), https://doi.org/10.1038/srep28533.
Access StatusOpen Access
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.
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