计算机工程与应用2024，Vol.60Issue(8)：148-155,8.DOI:10.3778/j.issn.1002-8331.2212-0301
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
摘要
Abstract
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 containing μ and β rhythms 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.关键词
脑电信号/运动想象/小波包重构/样本熵/共空间模式/卷积神经网络Key words
electroencephalogram/motor imagery/wavelet packet transform/sample entropy/common spatial pattern/convolution neural network分类
医药卫生引用本文复制引用
宋世林,张学军..脑电信号多特征融合与卷积神经网络算法研究[J].计算机工程与应用,2024,60(8):148-155,8.基金项目
国家自然科学基金(61977039). （61977039）
