Neural signal classification using a simplified feature set with nonparametric clustering

Authors:
Zhi Yang;Qi Zhao;Wentai Liu
Affiliations:
School of Engineering, University of California at Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA;School of Engineering, University of California at Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA;School of Engineering, University of California at Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA
Venue:
Neurocomputing
Year:
2009

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Abstract

This paper presents a spike sorting method using a simplified feature set with a nonparametric clustering algorithm. The proposed feature extraction algorithm is efficient and has been implemented with a custom integrated circuit chip interfaced with the PC. The proposed clustering algorithm performs nonparametric clustering. It defines an energy function to characterize the compactness of the data and proves that the clustering procedure converges. Through iterations, the data points collapse into well formed clusters and the associated energy approaches zero. By claiming these isolated clusters, neural spikes are classified.