Neural signal classification using a simplified feature set with nonparametric clustering

Neurocomputing

Volumn 73, Issue 1-3, 2009, Pages 412-422

  Yang, Zhi ^a   Zhao, Qi ^a   Liu, Wentai ^a  

^a University of California Santa Cruz   (United States)

Author keywords

Action potential; Clustering; Spike feature extraction; Spike sorting

Indexed keywords

ACTION POTENTIAL; CLUSTERING; CLUSTERING PROCEDURE; CUSTOM-INTEGRATED CIRCUITS; DATA POINTS; ENERGY APPROACH; ENERGY FUNCTIONS; FEATURE EXTRACTION ALGORITHMS; FEATURE SETS; ISOLATED CLUSTER; NEURAL SIGNALS; NEURAL SPIKE; NON-PARAMETRIC; SPIKE FEATURE EXTRACTION; SPIKE SORTING;

FEATURE EXTRACTION; INTEGRATED CIRCUITS; SET THEORY; SORTING;

CLUSTERING ALGORITHMS;

ACCURACY; ACTION POTENTIAL; ALGORITHM; ARTICLE; CLASSIFICATION; CLUSTER ANALYSIS; COMPUTER PROGRAM; CONTROLLED STUDY; MATHEMATICAL COMPUTING; MICROCOMPUTER; NERVE CELL NETWORK; NONPARAMETRIC TEST; PRIORITY JOURNAL; SIGNAL DETECTION; SIGNAL NOISE RATIO; SPIKE WAVE;

EID: 70350710027     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2009.07.013     Document Type: Article

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