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Efficient neural spike sorting using data subdivision and unification

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journal contribution
posted on 2021-02-10, 00:00 authored by M U Hassan, Rakesh Veerabhadrappa, Asim BhattiAsim Bhatti
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.

History

Journal

PLoS ONE

Volume

16

Issue

2

Article number

ARTN e0245589

Pagination

1 - 14

Publisher

Public Library Science

Location

San Francisco, Calif.

ISSN

1932-6203

eISSN

1932-6203

Language

English

Publication classification

C1 Refereed article in a scholarly journal