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Sparse representation-based classification scheme for motor imagery-based brain-computer interface systems

机译：基于运动图像的脑机接口系统的基于稀疏表示的分类方案

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摘要

Motor imagery (MI)-based brain-computer interface systems (BCIs) normally use a powerful spatial filtering and classification method to maximize their performance. The common spatial pattern (CSP) algorithm is a widely used spatial filtering method for Mi-based BCIs. In this work, we propose a new sparse representation-based classification (SRC) scheme for Mi-based BCI applications. Sensorimotor rhythms are extracted from electroencephalograms and used for classification. The proposed SRC method utilizes the frequency band power and CSP algorithm to extract features for classification. We analyzed the performance of the new method using experimental datasets. The results showed that the SRC scheme provides highly accurate classification results, which were better than those obtained using the well-known linear discriminant analysis classification method. The enhancement of the proposed method in terms of the classification accuracy was verified using cross-validation and a statistical paired t-test (p < 0.001).
机译：基于运动图像（MI）的脑机接口系统（BCI）通常使用功能强大的空间过滤和分类方法来最大化其性能。通用空间模式（CSP）算法是基于Mi的BCI的一种广泛使用的空间滤波方法。在这项工作中，我们为基于Mi的BCI应用提出了一种新的基于稀疏表示的分类（SRC）方案。从脑电图提取感觉运动节律并用于分类。提出的SRC方法利用频带功率和CSP算法提取特征进行分类。我们使用实验数据集分析了该新方法的性能。结果表明，SRC方案提供了高度准确的分类结果，比使用众所周知的线性判别分析分类方法获得的结果要好。使用交叉验证和统计配对t检验验证了所提方法在分类准确性方面的增强（p <0.001）。

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  • 来源
    《Journal of neural engineering》 |2012年第5期|p.0560021.1-0560021.12|共12页
  • 作者

    Younghak Shin; Seungchan Lee; Junho Lee; Heung-No Lee;

  • 作者单位

    School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju,Korea;

    School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju,Korea;

    School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju,Korea;

    School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju,Korea;

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