Abstract
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.
Highlights
Neuro-engineering is an interdisciplinary research domain that provides a collaborative platform for engineers, scientists, neurologists and clinicians to grow a robust and reliable communication network between human brain and computers using advanced engineering procedures, methods, tools and algorithms [1,2,3]
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
We considered two scenarios; 1) when the Z score distribution of the Euclidean distance matrix (EDM) matrix is normal and 2) when the Z score distribution of the EDM matrix is skewed
Summary
Neuro-engineering is an interdisciplinary research domain that provides a collaborative platform for engineers, scientists, neurologists and clinicians to grow a robust and reliable communication network between human brain and computers using advanced engineering procedures, methods, tools and algorithms [1,2,3]. The dependency of speed and computational time on data size in spike-sorting has made it very difficult to efficiently and accurately identify the total number of neurons in large and dense electrophysiological data. The proposed algorithm preprocesses data to significantly reduce computational time and to enhance speed and efficiency of a wide range of existing spike sorting algorithms. The novelty of the proposed mechanism lies in its capability to operate the existing spike sorting algorithms at their peak efficiency by introducing the optimal length subsets of large electrophysiological data at clustering stage. All the featured spikes, having the Euclidean distance lies within the OF range, are considered in estimating the bounded region in Eq (3) for unification of sub clusters. In this work clustering accuracy, as is described in [86] and (13), is adopted as a validation index and is calculated using the confusion matrix [87] as in (14)
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