基于粒子群优化支持向量机康复下肢外骨骼的脑电控制研究

为解决失能人群自主移动的问题，脑机接口(brain computer interface, BCI)已广泛应用于外骨骼领域，但脑电(electroencephalogram, EEG)信号因信噪比低等原因导致识别率一直难以提高。为提高基于脑机接口下肢外骨骼的信号识别率，采用粒子群优化支持向量机(particle swarm optimization-support vector machine, PSO-SVM)算法提高脑电信号识别率，取得了86.52%的脑电信号识别率。首先建立共空间模式(common spatial pattern, CSP)数学模型对脑电信号进行特征提取，随后建立基于粒子群优化的支持向量机分类模型，优化脑电信号分类关键参数，将最终的实验数据与传统的支持向量机分类方法比较，最后进行算法的验证及下肢外骨骼实验。实验结果表明：经过粒子群优化的支持向量机分类准确明显高于传统支持向量机分类。所提出粒子群优化支持向量机对脑电信号的特征识别方法可实现运动想象(motor imagery, MI)的精确识别，为脑机接口技术在康复外骨骼领域的应用提供理论基础和技术支持。

Motor Imagery Classification Based on PSO-SVM and Its BCI Control in Lower Limb Exoskeleton

In order to solve the problem of autonomous movement of disabled people Brain computer Interface (BCI) has been applied in the exoskeleton widely. In the practical use, the low signal-noise ratio of electroencephalogram (EEG) signal results in the low classification accuracy in BCI. In order to improve the signal recognition rate of lower limb exoskeleton based on brain computer interface, particle swarm optimization support vector machine (PSO-SVM) algorithm is used to improve the EEG signal recognition rate, and 86.52% EEG signal recognition rate is achieved. This paper firstly establishes common spatial pattern (CSP) mathematical model for feature extraction of EEG signals, and then establishes a particle PSO-SVM classification model. Secondly, the key parameters of EEG classification are optimized, and the final experimental data are compared with the traditional SVM classification method. Finally, the algorithm is verified and the lower limb exoskeleton experiment is carried out. The experimental results show that the classification accuracy of PSO-SVM is significantly higher than that of traditional SVM, and the average classification result can reach 86.52%, which improves the recognition rate of motor imagery (MI) EEG signals. This paper proposes a method of feature recognition of MI signals based on PSO-SVM, which can realize the accurate recognition of MI, and provide theoretical basis and technical support for the application of brain computer interface technology in the field of exoskeleton.