中南民族大学学报（自然科学版）2018，Vol.37Issue(1)：85-89,5.
基于ICA和极限学习机的模拟阅读脑电特征分类
EEG Feature Classification of Imitating-Reading Based on ICA and Extreme Learning Machine
摘要
Abstract
In order to effectively extract the N2-P3 components,ICA was introduced;At the same time,Extreme Learning Machine(ELM)was used as the classifier. The EEG data of 7 subjects were recorded, the ICA was used to separate the high-dimensional EEG data from each subject, and the N2-P3 component was extracted as the target Characteristics, and with untargeted features into the ELM for classification. The training time and classification accuracy of the seven subjects were trained and compared with the SVM. The results show that, after the ICA feature extraction, the training time is greatly reduced. In the classificationaccuracy, Compared with SVM, classification accuracy of ICA + ELM has a more substantial increase,from the latter average of 82.4% to 97.7%.关键词
模拟阅读/N2-P3成分/极限学习机Key words
imitating-reading/N2-P3/ELM分类
信息技术与安全科学引用本文复制引用
官金安,杨建华,赵瑞娟..基于ICA和极限学习机的模拟阅读脑电特征分类[J].中南民族大学学报（自然科学版）,2018,37(1):85-89,5.基金项目
国家自然科学基金资助项目(91120017) （91120017）
