[1]曲思霖,王从庆,李建亮,等.基于小波变换共空间模式的脑电信号解码[J].郑州大学学报(工学版),2022,43(03):31-36.
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基于小波变换共空间模式的脑电信号解码()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
43
期数:
2022年03期
页码:
31-36
栏目:
出版日期:
2022-04-10

文章信息/Info

Title:
EEG Decoding Based on Wavelet Transform and Common Space Pattern
作者:
曲思霖王从庆李建亮展文豪张 民
文献标志码:
A
摘要:
针对运动想象脑电信号实现任务少、识别准确率低等问题,提出了一种基于小波包分解的共空间模式脑电信号特征提取方法。该方法通过长短期记忆网络进行脑电信号解码,采用独立成分分析的方法将运动想象信号进行盲源分离,采用小波包分解方法将每个通道脑电信号按频率分为8组。计算每组信号的功率值,采用递归特性消除方法去除对分类不重要的10个节点特征,将被选择的节点信号采用1对1共空间模式提取空域特征,将特征矩阵输入长短期记忆网络进行脑电信号解码,得到4类运动想象信号分类结果。采用本文方法对公开的脑机接口竞赛数据集(包括左手想象信号、右手想象信号、舌头想象信号、双脚想象信号)前3位受试者数据进行验证,结果表明:本文方法的识别准确率分别为90.28%,94.25%、96.55% ,平均识别准确率达到93.69%。与其他方法对比,本文方法识别准确率较高。用识别的脑电信号作为解码控制信号,控制虚拟太空环境中的空间机械臂顺时针或逆时针运动,达到抓取空间中目标物体的目的。
Abstract:
Aiming at the problem of fewer tasks and low accuracy of recognition for motion imagination electro-encephalogram(EEC) signals,in this paper a common space pattern (CSP) method based on wavelet packetdecomposition( WPD) was proposed to extract the features of EEG signals. The long short term memory net-work was used to decode the EEG signals. Motor imagination signals were separated from blind sources by in-dependent component analysis(ICA ) , and each chamnel EEG signal was divided into 8 groups by frequency u-sing wavelet packet decomposition.The power value of each group of signals was calculated,10 features wereremoved for classification by recurrence fealure elimination(RFE). The selected signals were extracted by one-to-one common space pattern filters. The feature matrix were input into a long shortterm memory network forEEG decodimg, and the classification results of 4 categories of motion imagination signals were obtained.Theproposed method was used to verify the open data set of brain computer interface( BCI) competition ( includingfour kinds of EEG signals :left hand imagination signal ,right hand imagination signal ,tongue imagination sig-nal,and foot imagination signal ) . The recognition accuracy of three subjects was90.28% ,94.25% and 96.55% respectively , and the average recognition accuracy could reach 93.69% .Compared with other featureextraction and classification methods ,this method had a high classification accuracy. The decoded EEG sig-nals were used to control the clockwise or counterclockwise movement of the space manipulator to achieve thepurpose of grasping the farget in the virtual space environment.
更新日期/Last Update: 2022-05-02