¹School of Textile, Tianjin Polytechnic University, Tianjin, China
²Key Laboratory for Advanced Textile Composite of Ministry of Education, Tianjin Polytechnic University, Tianjin, China
³Institute of Textiles and Clothing, Hong Kong Polytechnic University, Hong Kong, China

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Abstract—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.

Index Terms—FKCM, SVM, classification, recognition, biopotential surface electrode

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Cite: Hao Liu, Xiaoming Tao, Pengjun Xu, Guanxiong Qiu, "Classification of Bio-potential Surface Electrode based on FKCM and SVM," Journal of Software vol. 6, no. 5, pp. 880-886, 2011.