Volume 1 Number 4 (Nov. 2012)
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IJCCE 2012 Vol.1(4): 331-336 ISSN: 2010-3743
DOI: 10.7763/IJCCE.2012.V1.85

Research on the Fractal Feature Extraction Based SSVEP Idle-State Detection

Xiuquan Li and Zhidong Deng

Abstract—Presented study investigates nonlinear feature extraction method for Electroencephalography (EEG) signal using fractal measures. The classical fractal measures of Higuchi dimension and Box-counting dimension were used and compared. This paper also introduces a new measure of approximate fractal entropy (AFE). It is applied as feature extraction in solving the problem of SSVEP idle-state detection. Comparison study was conducted between AFE and fractal dimension methods. Experimental results show the advantage of AFE-based feature extraction.

Index Terms—Electroencephalography (EEG), steady-state visual evoked potentials (SSVEPs), feature extraction, brain-computer interface (BCI).

Xiuquan Li is with the Chinese Academy of Science and Technology for Development, Beijing, 100038, China (e-mail:lixiuquan@tsinghua.org.cn).
Zhidong Deng is with the State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China (e-mail: michael@tsinghua.edu.cn)

Cite: Xiuquan Li and Zhidong Deng, "Research on the Fractal Feature Extraction Based SSVEP Idle-State Detection," International Journal of Computer and Communication Engineering vol. 1, no. 4, pp. 331-336 , 2012.

General Information

ISSN: 2010-3743 (Online)
Abbreviated Title: Int. J. Comput. Commun. Eng.
Frequency: Quarterly
Editor-in-Chief: Dr. Maode Ma
Abstracting/ Indexing: INSPEC, CNKI, Google Scholar, Crossref, EBSCO, ProQuest, and Electronic Journals Library
E-mail: ijcce@iap.org
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