[Computational-biology] A Graph-Laplacian-Based Feature Extraction Algorithm for Neural Spike Sorting

Yasser via comp-bio%40net.bio.net (by y.ghanbari from gmail.com)
Wed Nov 4 15:42:06 EST 2009


Analysis of extracellular neural spike recordings is highly dependent
upon the accuracy of neural waveform classification, commonly referred
to as spike sorting. Feature extraction is an important stage of this
process because it can limit the quality of clustering which is
performed in the feature space. This paper proposes a new feature
extraction method (which we call Graph Laplacian Features, GLF) based
on minimizing the graph Laplacian and maximizing the weighted
variance. The algorithm is compared with Principal Components Analysis
(PCA, the most commonly-used feature extraction method) using
simulated neural data. The results show that the proposed algorithm
produces more compact and well-separated clusters compared to PCA. As
an added benefit, tentative cluster centers are output which can be
used to initialize a subsequent clustering stage.

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