Classification of bio-potential surface electrode based on FKCM and SVM

Hao Liu, Xiaoming Tao, Pengjun Xu, Guanxiong Qiu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

7 Citations (Scopus)


In this paper, a method which is used for evaluating the performance of bio-potential surface electrode (BSE) with multi-index is presented. The Fuzzy kernel C-means (FKCM) algorithm and KF statistic are employed for classifying the BSE samples and searching an optimal classification amount respectively. Subsequently, a discriminant function is constructed by support vector machines (SVM) for recognizing the new measured samples. Experimental result shows classification correction ratios of improved FKCM algorithm are 96.3% and 85% on the IRIS and BSE dataset according a priori knowledge, furthermore, the recognition correction ratios of SVM algorithm are 96.3% and 90% on the IRIS and BSE dataset.
Original languageEnglish
Pages (from-to)880-886
Number of pages7
JournalJournal of Software
Issue number5
Publication statusPublished - 20 Jul 2011


  • Biopotential surface electrode
  • Classification
  • FKCM
  • Recognition
  • SVM

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Artificial Intelligence


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