Graph-Spectrum-Based Neural Spike Features for Stereotrodes and Tetrodes
- From: Yasser <y.ghanbari@xxxxxxxxx>
- Date: Tue, 15 Dec 2009 19:22:56 -0800 (PST)
Graph-Spectrum-Based Neural Spike Features for Stereotrodes and
Tetrodes
PDF: http://lyle.smu.edu/~yghanbari/ICASSP2010_YG.pdf
ABSTRACT
Extracellular recording of neural signals records the action
potentials (known as spikes) of neurons adjacent to the electrode as
well as the noise generated by the overall neural activity around the
electrode. Analysis of these spikes 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 introduces a new feature
extraction algorithm for neural spike sorting to isolate single
neuronal units out of multi-unit activities when more than one channel
(two channels in stereotrode and four in tetrode) are used in the
recording electrode. The proposed algorithm, which is inspired by the
spectral graph theory, simultaneously minimizes the graph-Laplacian
and maximizes the variance. Real test signals from stereotrode and
tetrode recordings show that the proposed approach outperforms the
most commonly-used feature extraction methods including Principal
Components Analysis (PCA) and spike amplitude (peak-to-peak) ratios
between different channels of electrode.
Index Terms— Biological signal processing, feature extraction, neural
spike sorting, stereotrode, tetrode
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