基于PCA融合PSO-SVM的运动想象脑电信号分类方法

Classification of Motor EEG Signals Based on PCA and PSO-SVM

运动想象脑电信号的分类识别是当前脑机接口(BCI)技术面临的难点。针对该问题,提出一种融合主成分分析(PCA)和粒子群优化-支撑向量机(PSO-SVM)的运动想象脑电信号分类方法。首先利用PCA对采集到的高维脑电信号进行分析,剔除其中噪声分量并提取三维反应不同脑电信号差异特性的特征向量。然后利用SVM对特征向量进行分类,同时针对SVM分类性能受核参数影响较大的问题,利用PSO算法的全局寻优能力对其进行优化,从而提升SVM的分类性能。最后采用BCI竞赛中所用Graz数据进行实验,结果表明所提的PCA融合PSO-SVM方法可以获得95.3%的分类性能,在低信噪比条件下具有鲁棒性和较高的应用前景。

The feature extraction,classification and recognition of electroencephalogram signals of motor imagination are the difficult problems faced by the current Brain Computer Interface(BCI)technology.Aiming at this problem,this paper proposes a classification method of motor imaging EEG signals combining Principal Component Analysis(PCA)and Particle Swarm Optimization optimized-Support Vector Machine(PSO-SVM).Firstly,PCA is used to reduce the dimension of the collected high-dimensional electroencephalogram signal,eliminating the noise components and extracting the feature vectors reflecting the different characteristics of three-dimensional EEG signals.Then SVM is used to classify the feature vectors.In view of the problem that the SVM classification performance is greatly affected by the kernel parameters,the global optimization ability of PSO algorithm is used to optimize the SVM classification performance so as to improve the SVM classification performance.Finally,the Graz data used in the BCI competition is used for experiments.The results show that the proposed PCA fusion PSO-SVM method can obtain 95.3%classification performance,and has a high application prospect.