基于RSCM与黎曼空间的运动想象脑电分类研究
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1.北京工商大学计算机与人工智能学院 北京 100048;2.北京工商大学中国轻工业工业互联网与大数据重点实验室 北京 100048

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TN911.7

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国家自然科学基金项目(62173007)、2024北京工商大学研究生教育教学成果培育项目(XYCGPYLX2024006)资助


Research on motor imagery EEG classification based on RSCM and Riemann space
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1.School of Computer and Artificial Intelligence, Beijing Technology and Business University,Beijing 100048, China; 2.Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University,Beijing 100048, China

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    摘要:

    近年来,运动想象(MI)在辅助医疗和人机交互领域备受关注。然而,经典的共空间模式(CSP)特征提取方法主要基于时域信号计算协方差矩阵(CM),易受噪声和伪迹干扰,且无法充分利用脑电信号(EEG)的频谱信息,导致分类精度与稳定性不足。为了解决这一问题,本研究提出了一种基于正则化频谱协方差矩阵(RSCM)与黎曼空间的MI-EEG分类算法。首先,对预处理后的EEG信号进行快速傅里叶变换,计算频谱协方差矩阵,并进行岭正则化;然后,将正则化矩阵映射到切空间中进行平滑滤波,并将结果映射回黎曼空间以提取CSP特征;最后,采用支持向量机(SVM)完成分类任务。实验结果表明,在BCI竞赛IV数据集1和2a上,本研究方法的二分类平均准确率分别达到了86.95%和81.48%,较传统CSP分别提升了7.44%和9.57%;在BCI竞赛IV数据集2a上,本研究方法的四分类平均准确率达到了74.23%,较传统CSP方法提升了14.10%。实验结果表明,本研究方法在MI-EEG分类中具有有效性。

    Abstract:

    In recent years, motor imagery (MI) has attracted significant attention in the fields of assistive healthcare and human-computer interaction. However, the classical common spatial pattern (CSP) feature extraction method is mainly based on calculating covariance matrices (CM) from time-domain signals, making it susceptible to noise and artifacts while failing to fully exploit the spectral information of electroencephalogram (EEG) signals. This limitation reduces classification accuracy and stability. To address this problem, this study proposes a MI-EEG classification algorithm based on regularized spectral covariance matrix (RSCM) and Riemannian space. Firstly, the preprocessed EEG signals undergo fast Fourier transform (FFT) to compute spectral covariance matrices, followed by ridge regularization. Then, the regularized matrices are mapped into the tangent space for geodesic filtering and projected back to the Riemannian space for CSP feature extraction. Finally, classification is performed using SVM. Experimental results demonstrate that, on BCI Competition IV datasets 1 and 2a, the proposed method achieves an average binary classification accuracy of 86.95% and 81.48%, respectively, outperforming traditional CSP by 7.44% and 9.57%. In the four-class classification task on BCI Competition IV dataset 2a, it reaches 74.23%, representing a 14.10% improvement over traditional CSP. These findings indicate the effectiveness of the proposed method in MI-EEG classification.

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廉小亲,刘春权,高超,邓子谦,吴叶兰.基于RSCM与黎曼空间的运动想象脑电分类研究[J].电子测量技术,2025,48(9):84-93

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  • 在线发布日期: 2025-05-23
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