Hierarchical Adaptive Means (HAM) Clustering for Hardware-Efficient, Unsupervised and Real-time Spike Sorting.
File(s)
Accepted version
Author(s)
Paraskevopoulou, SE
Wu, D
Eftekhar, A
Constandinou, TG
Type
Journal Article
Abstract
This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation.
Date Issued
2014-07-15
Date Acceptance
2014-07-03
Citation
Journal of Neuroscience Methods, 2014, 235, pp.145-156
ISSN
1872-678X
Publisher
Elsevier
Start Page
145
End Page
156
Journal / Book Title
Journal of Neuroscience Methods
Volume
235
Copyright Statement
© 2014, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Identifier
PII: S0165-0270(14)00248-9
Subjects
Brain machine interface
Clustering
Hardware implementable
Realtime
Spike sorting
Streaming
Publication Status
Published