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．
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.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2007年第10期1237-1241,共5页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金资助项目(30670660)
陕西省科技计划资助项目(2006k15-G5)
关键词
脑电
生物反馈
样本熵
支持向量机
electroencephalography
biofeedback
sample entropy
support vector machine
