Characterization and Classification of EEG Attention Based on Fuzzy Entropy

Author

Xu, Luqiang ; Liu, Jingxia ; Xiao, Guangcan ; Jin, Weidong

fYear

2012

fDate

July 31 2012-Aug. 2 2012

Firstpage

277

Lastpage

280

Abstract

Attention recognition is an essential component in many biofeedback applications. Many biofeedback training need attention recognition algorithm to calculate concentration quantification. This paper propose an fuzzy entropy (FuzzyEn) to extract attention level feature from EEG. The developed method was compared with other methods used for the concentration level recognition. EEG data collected from twelve healthy subjects. Experimental results demonstrate that average identification rate of FuzzyEn feature extraction method reaches 81%. The result demonstrated an efficiency of the proposed approach.

Keywords

behavioural sciences computing; electroencephalography; fuzzy set theory; medical signal processing; signal classification; EEG attention; FuzzyEn feature extraction method; attention recognition algorithm; biofeedback applications; biofeedback training; concentration level recognition; concentration quantification; fuzzy entropy; Accuracy; Biological control systems; Electroencephalography; Entropy; Feature extraction; Games; Vectors; Approximate Entropy; Attention Level; EEG; Fuzzy Entry;

fLanguage

English

Publisher

ieee

Conference_Titel

Digital Manufacturing and Automation (ICDMA), 2012 Third International Conference on

Conference_Location

GuiLin

Print_ISBN

978-1-4673-2217-1

Type

conf

DOI

10.1109/ICDMA.2012.67

Filename

6298307