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

Yasser via neur-sci%40net.bio.net (by y.ghanbari from gmail.com)
Sat Dec 12 02:36:33 EST 2009


PDF:
http://lyle.smu.edu/~yghanbari/EMBC09_YG.pdf

ABSTRACT:
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.

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.

PDF:
http://lyle.smu.edu/~yghanbari/DSP2009_Paper.pdf

ABSTRACT
Extracellular recording of neural signals records the action
potentials of neurons adjacent to the electrode as well as the noise
generated by the overall neural activity around the electrode. The
spike sorting process, i.e., detection of the noisy spikes in the
recorded digital signal, feature extraction, and clustering of the
spikes has been investigated extensively since it is a challenging
problem for neuroscientists. However, the effects of digitization,
including the sampling rate and number of bits, on the above three-
stage process have not been investigated. This paper addresses the
robustness of the spike sorting procedure to variations in the signal
bandwidth, sampling rate, and the number of quantization levels (bit
depth). Different signalto-noise ratios (SNRs) are used and their
effects on clustering are studied, when using principal components
analysis (PCA) features. The PCA-based features are shown to be robust
to quantization bit depth variations while they are quite sensitive to
the sampling rate even when it exceeds the Nyquist rate.



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