[Research on motor imagery recognition based on feature fusion and transfer adaptive boosting].

Q4 Medicine
Yuxin Zhang, Chenrui Zhang, Shihao Sun, Guizhi Xu
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引用次数: 0

Abstract

This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.

基于特征融合和转移自适应增强的运动图像识别研究
本文提出了一种基于特征融合和转移自适应增强(TrAdaboost)的运动图像识别算法,以解决跨受试者运动图像识别准确率低的问题,从而提高基于运动图像的脑机接口(BCI)在跨个体使用中的可靠性。利用自回归模型、功率谱密度和离散小波变换,可以获得MI的时频域特征,利用滤波器组公共空间模式提取空间域特征,利用多尺度色散熵提取非线性特征。采用第四届国际脑机接口大赛的IV-2a数据集进行二值分类任务,将改进的TrAdaboost集成学习算法与支持向量机(SVM)、k近邻(KNN)和基于思维进化算法的反向传播(MEA-BP)神经网络相结合构建模式识别模型。结果表明,基于svm的TrAdaboost综合学习算法在迁移30%的目标域实例数据时表现最佳,平均分类准确率为86.17%,Kappa值为0.723 3,AUC值为0.849 8。这些结果表明,该算法可用于跨个体的脑机接口信号识别,为提高脑机接口识别模型的泛化能力提供了一种新的途径。
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来源期刊
生物医学工程学杂志
生物医学工程学杂志 Medicine-Medicine (all)
CiteScore
0.80
自引率
0.00%
发文量
4868
期刊介绍:
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