基于Bayes判别法的脑电图数据分析的研究  

Study on Bayes Analysis of EEG Data

作　　者：史原[1] 刘瑞杰[1] 

机构地区：[1]大连科技学院,大连116028

出　　处：《价值工程》2015年第13期169-170,共2页Value Engineering

摘　　要：目的:本文通过对客观记录的受试者脑电图数据进行Bayes判别分析,判断其能否应用于脑电数据特征提取和分类决策。为脑电图研究的其它分析做基础分析。方法:根据α波的强弱不同将21导电极分为四类,分别对63例正常状态下受试者21导联电极的脑电图数据进行Bayes判别分析,并利用误判率回代估计法检验判别准确率。数据处理和统计分析采用独立设计的脑电图分析工具箱和Bayes判别分析程序。结果:表明对63例正常状态下受试者的脑电图数据进行Bayes判别分析,预测各电极分类准确率75.4%。结论:Bayes判别法预测准确率较高,脑电特征(主要为α波)提取较为准确,能较好的应用于脑电数据特征提取和分类决策中,从而辅助脑电图的检查和定量分析,为脑电图的检验提供有效的分析手段。ObjectiveIn this paper, we have done Bayes discriminant analysis to EEG data of experiment objects which are recorded impersonally, come up with a relatively accurate method used in feature extraction and classification decisions. The present study is the groundwork analysis for other analysis in EEG study. Methods：In accordance with the strength of α wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we have done Bayes discriminant analysis to EEG data of six objects. EEG data processing and statistic analysis adopted independently designed EEG analysis toolbox and the program of correlation analysis. Results：In use of part of EEG data of 63 people, we have done Bayes discriminant analysis, the electrode classification accuracy rates is 75.4%. Conclusions：Bayes discriminant has higher prediction accuracy, EEG features （mainlyαwave） extract more accurate. Bayes discriminant would be better applied to the feature extraction and classification decisions of EEG data.

关 键 词：脑电图 Bayes判别分析 Α节律 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]