Efficient neural spike sorting using data subdivision and unification.

Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activit...

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Main Authors: Masood Ul Hassan, Rakesh Veerabhadrappa, Asim Bhatti
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0245589
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author Masood Ul Hassan
Rakesh Veerabhadrappa
Asim Bhatti
author_facet Masood Ul Hassan
Rakesh Veerabhadrappa
Asim Bhatti
author_sort Masood Ul Hassan
collection DOAJ
description Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.
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spelling doaj.art-def4050a6bc8485b8036be680a3520a82022-12-21T23:31:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01162e024558910.1371/journal.pone.0245589Efficient neural spike sorting using data subdivision and unification.Masood Ul HassanRakesh VeerabhadrappaAsim BhattiNeural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.https://doi.org/10.1371/journal.pone.0245589
spellingShingle Masood Ul Hassan
Rakesh Veerabhadrappa
Asim Bhatti
Efficient neural spike sorting using data subdivision and unification.
PLoS ONE
title Efficient neural spike sorting using data subdivision and unification.
title_full Efficient neural spike sorting using data subdivision and unification.
title_fullStr Efficient neural spike sorting using data subdivision and unification.
title_full_unstemmed Efficient neural spike sorting using data subdivision and unification.
title_short Efficient neural spike sorting using data subdivision and unification.
title_sort efficient neural spike sorting using data subdivision and unification
url https://doi.org/10.1371/journal.pone.0245589
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AT rakeshveerabhadrappa efficientneuralspikesortingusingdatasubdivisionandunification
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