脑电信号多特征融合与卷积神经网络算法研究

Algorithm Research Based on Multi-Feature Fusion of EEG Signals with Convolutional Neural Networks

针对脑电信号(electroencephalogram,EEG)运动想象中单一特征无法多维表征信号中的信息导致的分类准确率不高的问题,提出一种基于样本熵和共空间模式特征融合的特征提取算法。算法先对原始脑电信号进行小波包分解,从中选择包含μ和β节律的分量进行重构,然后分别提取重构信号的样本熵和CSP(common spatial pattern,CSP)特征,将两者融合组成新的特征向量,使用所设计的一维卷积神经网络对其进行识别获得分类结果。所提方法在2003年BCI Dataset III中获得了91.66%的分类准确率,在2008年BCI Dataset A中获得了85.29%的平均分类准确率。与近年来文献中提出的多特征融合算法相比,准确率提高了7.96个百分点。

In order to address the issue of low classification accuracy in motor imagery of electroencephalogram(EEG)signals,a feature extraction algorithm based on sample entropy and common spatial pattern(CSP)feature fusion has been proposed.The algorithm initially performs wavelet packet decomposition on the raw EEG signal,selecting the compo-nents containingμandβrhythms for reconstruction.Subsequently,the sample entropy and CSP features of the recon-structed signal are separately extracted.These two features are then fused to create a new feature vector which is recog-nized using a one-dimensional convolutional neural network designs in the paper,to obtain the classification result.The proposes method achieves a classification accuracy of 91.66%on the BCI Dataset III in 2003 and an average classification accuracy of 85.29%on the BCI Dataset A in 2008.Comparing with multi-feature fusion algorithms proposed in recent literature,the accuracy is improved by 7.96 percentage points.