传感技术学报2016，Vol.29Issue(10)：1471-1477,7.DOI:10.3969/j.issn.1004-1699.2016.10.001
基于HHT的脑电信号在不同阅读模式下的识别与分类
Recognition and Classification of EEG Signal in Reading Mode Based on Hilbert-Huang Transformation
摘要
Abstract
Objective To distinguish different kinds of EEG signals from the high-dimensional and redundant mass EEG nonlinear-data by Powerlab. Methods Firstly,EEG signals were sampled from an experimenter’s scalp when the experimenter was reading different kinds of books(closing eyes,reading English books,reading poems and read⁃ing modern Chinese). Secondly,HHT transform(Hilbert-Huang Transform,HHT)and Support Vector Machine method were used to train and distinguish the model of closing eyes and other three kinds of reading patterns. Final⁃ly,the algorithm is optimized because of its frequent phenomenon-end issue that occurred during the Empirical Mode Decomposition and the results were analyzed. Results the Empirical Mode Decomposition based on polynomi⁃al fitting algorithm could be used to recognize largest amount of EEG signals by 70%. Conclusions The experimental results demonstrate that the Optimized HHT algorithm based on Empirical Mode Decomposition and polynomial fit⁃ting algorithm can effectively make use of the information from the mass EEG nonlinear-data signal and is suitable and practical method of classification for research.关键词
脑电信号分析/希尔伯特-黄变换/端点效应/经验模态分解/支持向量机Key words
EEG signal analysis/hilbert-huang transform/end issue/empirical mode decomposition/support vector machine分类
信息技术与安全科学引用本文复制引用
梅婉欣,徐莹,柯大观..基于HHT的脑电信号在不同阅读模式下的识别与分类[J].传感技术学报,2016,29(10):1471-1477,7.基金项目
国家自然科学（30800248，31300939）；浙江省公益技术研究社会发展项目（2016C33G2041024）；浙江省自然科学 （）
