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 pat-terns,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.
Abstract
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.
维普
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