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基于组合分类器的不同状态下脑电信号分类
Alternative TitleClassification of EEG Signals in Different States Based on Combined Classifier
张进1; 李伟1; 俞建成1; 徐东岑1; 杜秀兰2
Department海洋机器人卓越创新中心
Source Publication控制与决策
ISSN1001-0920
2019
Volume34Issue:5Pages:897-907
Indexed ByEI ; CSCD
EI Accession number20192707153107
CSCD IDCSCD:6490013
Contribution Rank1
Funding Organization国家自然科学基金项目(61473207, 61233013)
Keyword脑电信号分类 手臂操作 组合分类器 距离差值 修正函数 水下机器人操作
Abstract当手臂操作与脑电控制被同时应用到水下机器人操作中且操作人员处于不同作业状态时,针对使用单一脑电信号分类器无法获得较为理想的控制意图识别准确率问题,本文提出了使用组合分类器选取分类结果和根据实际作业情况的特殊性修正分类结果的方法来提升识别准确率.该方法首先使用Fisher判别方法分别对无手臂操作与存在手臂操作产生的数据进行训练得到两种作业状态下的分类器,其次将两分类器进行组合并使用曲线拟合的方式确定用来判定分类结果的基准距离差值(该差值的选取考虑了个体差异),然后根据实际作业情况的特殊性使用距离修正函数对距离差值进行修正,最后通过比较基准距离差值与修正后距离差值的大小来确定最终分类结果.为了验证所提方法的有效性,6位被试者被邀请参与了测试过程.实验结果显示,在设计的在线实验中该方法相对于其它三种方法在识别准确率上分别提升了13.42%、5.55%和5.55%,说明该方法是可行且有效的.
Other AbstractIt is difficult to use a single EEG classifier to achieve an ideal recognition accuracy of control intention when the operator is in different operating states and both arm operation and EEG control are used in the underwater vehicle operation. An algorithm is proposed in this paper to improve the recognition accuracy by selecting the classification result by using the combined classifier and correcting the classification result according to the specific situation of the actual operation. Firstly, the Fisher discriminant method is used to train the data generated by the armless operation and the arm operation to get the classifier in two operation states. Secondly, the two classifiers are combined and the curve fitting is used to determine the reference distance difference which is used to determine the classification result (the selection of the difference takes into account individual differences). Thirdly, the distance difference is corrected by the distance correction function according to the particularity of the actual operation situation. Finally, the final classification result is determined by comparing the difference between the reference distance difference and the corrected distance difference. In order to verify the effectiveness of the proposed algorithm, six subjects were invited to participate in the testing process. The experimental results show that the proposed algorithm improves the recognition accuracy by 13.42%, 5.55% and 5.55% respectively compared with the other three methods in the designed online experiment, which demonstrates that the algorithm is feasible and effective.
Language中文
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/21845
Collection海洋机器人卓越创新中心
Corresponding Author李伟
Affiliation1.中国科学院沈阳自动化研究所机器人学国家重点实验室;
2.天津大学电气自动化与信息工程学院
Recommended Citation
GB/T 7714
张进,李伟,俞建成,等. 基于组合分类器的不同状态下脑电信号分类[J]. 控制与决策,2019,34(5):897-907.
APA 张进,李伟,俞建成,徐东岑,&杜秀兰.(2019).基于组合分类器的不同状态下脑电信号分类.控制与决策,34(5),897-907.
MLA 张进,et al."基于组合分类器的不同状态下脑电信号分类".控制与决策 34.5(2019):897-907.
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