基于分数阶傅里叶变换的运动脑电信号分类方法

Classification Method of Motor EEG Signals Based on Fractional Fourier Transform

运动想象脑电信号作为一种典型的非线性、非平稳信号,在传统基于单一特征提取的分类方法中难以取得理想的分类性能。针对该问题,将分数阶傅里叶变换(Fractional Fourier Transform,FrFT)引入到脑电信号特征提取过程中。首先利用FrFT对信号进行分析,在扩展特征域的同时从不同维度提取信号中的有用信息并构成特征向量,然后利用支持向量机(Support Vector Machine,SVM)分类器对所提取的特征向量进行分类,最后采用Graz数据开展实验。实验结果表明所提方法能够获得高达92.57%的正确分类结果,明显高于传统采用单一特征提取的分类方法。

Motor imaging EEG signals are typical non-linear and non-stationary,thus the traditional classification method based on single feature extraction is difficult to achieve better classification performance.Aiming at this problem,the Fractional Fourier Transform(FrFT)is introduced into the feature extraction process of EEG signals.Firstly,FrFT is used to analyze signals,the useful information is extracted from different dimensions while expanding the feature domain,and the feature vectors is formed.Then Support Vector Machine(SVM)classifier is used to classify the proposed feature vectors.Finally,the experiment is carried out using Graz data.The experimental results show that the proposed method can achieve up to 92.57%correct classification results,which is significantly higher than the traditional classification method using single feature extraction.