Spike Sorting based on Improved Fuzzy C-means
LIU Han
LI Zhenxin
YU Yi
DONG Bingchao
Abstract：To propose an improved fuzzy clustering algorithm combined with fuzzy clustering algorithm and subtractive clustering method (SCM)to classify the detected spikes.The number and position of the clustering centers were obtained quickly by SCM,and then the obtained clustering centers as initial values were applied to the fuzzy clustering algorithm.The results indicated that this algo-rithm could reduce the dependence on initial clustering centers of fuzzy clustering algorithm,save the iterative computation time of fuzzy clustering algorithm and improve the operation efficiency.At the same time,this algorithm improved the classification accuracy of the spikes ,especially with the SNR being less than -30db.This algorithm is an excellent choice for the spike classification.
Keywords：Brain computer interface(BCI)Spike potential detectionPrincipal component analysisSpike potential classifica-tionSubtractive clustering method (SCM)Improved fuzzy C-means
Publication Date：2015-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：5( 41-44,48 )