Method for unsupervised classification of multiunit neural signal recording under low signal-to-noise ratio

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Kim, Kyung Hwan; Kim, Sung June
Issue Date
Institute of Electrical and Electronics Engineers (IEEE)
IEEE Trans. Biomed. Eng., vol. 50, pp. 421-431, April 2003
Mixture of Gaussiansneural spike sortingprojection pursuitunsupervised classification
Neural spike sorting is an indispensable step in the
analysis of multiunit extracellular neural signal recording. The
applicability of spike sorting systems has been limited, mainly to
the recording of sufficiently high signal-to-noise ratios, or to the
cases where supervised classification can be utilized. We present a
novel unsupervised method that shows satisfactory performance
even under high background noise. The system consists of an
efficient spike detector, a feature extractor that utilizes projection
pursuit based on negentropy maximization (Huber, 1985 and
Hyvarinen et al., 1999), and an unsupervised classifier based on
probability density modeling using mixture of Gaussians (Jain
et al., 2000). Our classifier is based on the mixture model with
a roughly approximated number of Gaussians and subsequent
mode-seeking. It does not require accurate estimation of the
number of units present in the recording and, thus, is better suited
for use in fully automated systems. The feature extraction stage
leads to better performance than those utilizing principal component
analysis and two nonlinear mappings for the recordings
from the somatosensory cortex of rat and the abdominal ganglion
of Aplysia. The classification method yielded correct classification
ratio as high as 95%, for data where it was only 66% when a
-means-type algorithm was used for the classification stage.
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Electrical and Computer Engineering (전기·정보공학부)Journal Papers (저널논문_전기·정보공학부)
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