基于特征融合的多分类运动想象脑电识别方法及应用  

Multi-classification Motor Imagery EEG Recognition Methodand Application Based on Feature Fusion

作　　者：张保旭 梁彤 孙田雪 魏笑 赵彦峻[1,2] ZHANG Bao-xu;LIANG Tong;SUN Tian-xue;WEI Xiao;ZHAO Yan-jun(School of Mechanical Engineering,Shandong University of Technology,Zibo 255049,China;Shandong Key Laboratory for Precision Manufacturing and Special Processing,Zibo 255049,China)

机构地区：[1]山东理工大学机械工程学院,淄博255049 [2]山东省精密制造与特种加工重点实验室,淄博255049

出　　处：《科学技术与工程》2024年第34期14742-14747,共6页Science Technology and Engineering

基　　金：国家自然科学基金(51905319);国家自然科学基金青年科学基金(51505263);山东省高等学校科技计划(J15LB08)。

摘　　要：为实现脑卒中患者下肢自主康复训练,外骨骼技术逐渐与脑机接口(brain computer interface, BCI)相结合,但多分类运动想象(motor imagery, MI)脑电信号(electroencephalogram, EEG)一直存在特征提取困难和识别准确率低的问题。故提出了一种基于小波独立成分分析(wavelet independent component correlation algorithm, WICA)和共空间模式(common spatial patterns, CSP)的脑电信号多分类优化支持向量机算法(support vector machine, SVM)。该方法使用基于粒子群优化(particle swarm optimization, PSO)的支持向量机进行分类识别。研究结果表明,该方法平均分类准确率相比于其他方法有较大提高,证明了该算法可以有效提取脑电特征,并具有较好的运动想象脑电信号识别效果。同时,通过运动想象与外骨骼装置结合,验证了在线实时进行脑电控制的可行性。In order to achieve autonomous lower limb rehabilitation training for stroke patients,exoskeletons are gradually combined with BCI(brain computer interface).Still,multi-class MI(motor imagery)EEG(electroencephalogram)has always had characteristics of difficulty in extraction and low recognition accuracy.A multi-classification optimized support vector machine algorithm for EEG signals based on WICA(wavelet independent component analysis)and CSP(common spatial patterns)was proposed.PSO(particle swarm optimization)was used to train a support vector machine for classification.It proves that the algorithm can effectively extract EEG features and has a better recognition effect of motor imagery EEG signals.The feasibility of online and real-time EEG control was verified by combining motor imagery and exoskeleton devices.

关 键 词：运动想象 独立成分分析 共空间模式 支持向量机 下肢外骨骼 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]