Characterization and classification of EEG attention level

doi:10.3724/SP.J.1087.2012.03268

Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (11): 3268-3270.DOI: 10.3724/SP.J.1087.2012.03268

1. College of Computer Science and Technology, Southwest University of Science and Technology, Mianyang Sichuan 621010, China
2. College of Information Science and Technology, Southwest Jiaotong University, Chengdu Sichuan 610031,China
3. College of Physics and Electronic Engineering,Sichuang Normal University，Chengdu Sichuan 610066, China
4.

Received:2012-04-18 Revised:2012-05-22 Online:2012-11-12 Published:2012-11-01
Contact: XU Lu-qiang

脑电注意水平的特征识别

徐鲁强¹,²,刘静霞³,肖光灿²,金炜东⁴

1. 西南交通大学信息科学与技术学院，成都 610031
2. 西南科技大学计算机科学与技术学院，四川绵阳 621010
3. 四川师范大学物理与电子工程学院,成都 610066
4. 西南交通大学

通讯作者: 徐鲁强
作者简介:徐鲁强 (1968-)，男，四川德阳人，副教授，博士，CCF会员，主要研究方向：智能识别；刘静霞(1971-)，女，重庆人，副教授，主要研究方向：信号处理；肖光灿（1956-）男，教授，四川绵阳人，主要研究方向: 模糊数学、动力系统；金炜东(1959-), 男, 安徽桐城人, 教授, 博士生导师, 主要研究方向：智能信息处理、满意度优化。

Abstract

Abstract: This paper proposed improved fuzzy entropy (FuzzyEn) to calculate attention levels from single channel EEG, on the basis of approximate entropy. EEG attention level signals collected from twelve healthy subjects were characterized by FuzzyEn and other methods, and Support Vector Machine (SVM) was used to classify all EEG attention levels. The experimental results demonstrate that average identification rate of FuzzyEn feature extraction method reaches 76.3%, and the fuzzy entropy method can effectively characterize the complexity of EEG attention level.

Key words: ElectroEncephaloGraphy (EEG), attention level, approximate entropy, fuzzy entry, fuzzy membership

摘要： 为了提高从单通道脑电信号中注意水平的识别精度，在近似熵基础上提出改进的模糊熵计算方法，用于计算脑电注意力水平值。以12例受试者脑电监测数据作为样本,提取脑电数据模糊熵特征值，采用支持向量机进行识别，并与其他方法进行比较，基于模糊熵的特征提取方法平均准确率达76.3%。实验结果表明，该模糊熵方法能有效地表征脑电注意力集中程度的复杂度。

关键词: 脑电波, 注意力水平, 近似熵, 模糊熵, 模糊隶属度

CLC Number:

徐鲁强刘静霞肖光灿金炜东. 脑电注意水平的特征识别[J]. 计算机应用, 2012, 32(11): 3268-3270.

References

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