Feature Exaction and Classification of Attention Related Electroencephalographic Signals Based on Sample Entropy

燕楠; 王珏; 魏娜; 宗良

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Feature Exaction and Classification of Attention Related Electroencephalographic Signals Based on Sample Entropy

更新时间：2025-12-08

- Feature Exaction and Classification of Attention Related Electroencephalographic Signals Based on Sample Entropy
- Vol. 41, Issue 10, Pages: 1237-1241(2007)
- 作者机构：
  
  西安交通大学生物医学信息工程教育部重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： R318.4
- Online First：10 October 2007，
  
  Published：2007
- 稿件说明：
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燕楠, 王珏, 魏娜, et al. Feature Exaction and Classification of Attention Related Electroencephalographic Signals Based on Sample Entropy[J]. 2007, 41(10): 1237-1241.
DOI：

燕楠, 王珏, 魏娜, et al. Feature Exaction and Classification of Attention Related Electroencephalographic Signals Based on Sample Entropy[J]. 2007, 41(10): 1237-1241. DOI：

摘要

提出一种用样本熵作为特征进行注意力相关脑电信号的分析与分类处理、并采用支持向量机(SVM)算法实现分类器的方法.7位年龄在20～30岁之间的男性受试者接受了执行3种不同注意任务状态下的测试.数据分析结果显示:样本熵分类法对注意任务相关脑电信号分类的正确率可达85.5%

优于传统频段能量法获得的分类精度(77.9%).这个结果暗示了样本熵能有效地识别出自发脑电中注意力相关信息

因而它可在脑电生物信息反馈治疗系统设计中获得广泛的应用.

Abstract

A method regarding the sample entropy(SampEn)as features is proposed to carry out the analysis and classification of attention related electroencephalographic(EEG)signals

and the support vector machine(SVM)algorithm is used as classifiers for classification. seven males(aged from 20 to 30)are recruited to perform three attention-related tasks

including attention

inattention

and relaxation states. The processing results demonstrate that the classification accuracy of the SampEn gets up to 85.5% for classifying the relation between attention and inattention

obviously much higher than that with frequency band power(77.9%). It indicates that the SampEn is more effective to extract the information attention-related in EEG to show the clinical application prospects in EEG biofeedback systems.

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references

Ebrahimi T, Vesin J M. Brain-computer interface in multimedia communication [J]. IEEE Signal Processing Magazine, 2003, 20(1):14-24.

Phillips H. Controlling pain by watching your brain [J]. New Scientist, 2004, 182(2445):9.

McFarland D J, McCane L M. EEG-based communication and control: short-term role of feedback [J]. IEEE Transactions on Rehabilitation Engineering, 1998, 6(3):7-11.

Green C S, Bavelier D. Action video game modifies visual selective attention [J]. Nature, 2003,423(6939):534-537.

王珏, 魏娜, 张彤,等. 注意力缺陷障碍伴多动症诊断技术的现状与展望 [J]. 中国康复医学杂志, 2004,19(5):392-394.

Richman J S, Moorman J R. Physiologica time-series analysis using approximate entropy and sample entropy [J]. Am J Physio: Heart Circ Physio,2000, 278(6):2039-2049.

Ab'asolo1 D, Hornero1 R, Espino P, et al. Entropy analysis of the EEG background activity inAlzheimer's disease patients [J]. Physiological Measurement, 2006, 27(3):241-253.

刘慧, 和卫星, 陈晓平. 生物时间序列的近似熵和样本熵方法比较 [J]. 仪器仪表学报,2004,25(4):806-807.

Liu Hui, He Weixing, Chen Xiaoping. Approximate entropy and sample entropy for physiological time-series [J]. Chinese Journal of Scientific Instrument,2004,25(4):806-807.

Vapnik V N. Statistical learning theory [M]. New York: John Wiley and Sons Inc., 1998.

Pincus S M, Goldberger A L. Physiological time-series analysis: what does regularity quantify? [J]. Am J Physiol: Heart Circ Physiol,1994, 266(4):643-656.

刘建平, 贺太纲, 郑崇勋,等. EEG 复杂性测度用于大脑负荷状态的研究 [J]. 生物医学工程学,1997, 14(1):33-37.

Liu Jianping, He Taigang, Zheng Chongxun, et al. Measuring EEG complexity for studying the state of mental load [J]. Journal of Biomedical Engineering,1997, 14(1):33-37.

LaBerge D. Attention, awareness, and triangular circuit [J]. Consciousness and Cognition,1997, 6(3):149-181.

薛建中. 基于自发脑电的脑-计算机接口研究[D].西安:西安交通大学生命科学与技术学院,2003.

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