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The CSP-Based New Features Plus Non-Convex Log Sparse Feature Selection for Motor Imagery EEG Classification

机译：基于CSP的新功能以及电机图像EEG分类的非凸起日志稀疏功能选择

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The common spatial pattern (CSP) is a very effective feature extraction method in motor imagery based brain computer interface (BCI), but its performance depends on the selection of the optimal frequency band. Although a lot of research works have been proposed to improve CSP, most of these works have the problems of large computation costs and long feature extraction time. To this end, three new feature extraction methods based on CSP and a new feature selection method based on non-convex log regularization are proposed in this paper. Firstly, EEG signals are spatially filtered by CSP, and then three new feature extraction methods are proposed. We called them CSP-wavelet, CSP-WPD and CSP-FB, respectively. For CSP-Wavelet and CSP-WPD, the discrete wavelet transform (DWT) or wavelet packet decomposition (WPD) is used to decompose the spatially filtered signals, and then the energy and standard deviation of the wavelet coefficients are extracted as features. For CSP-FB, the spatially filtered signals are filtered into multiple bands by a filter bank (FB), and then the logarithm of variances of each band are extracted as features. Secondly, a sparse optimization method regularized with a non-convex log function is proposed for the feature selection, which we called LOG, and an optimization algorithm for LOG is given. Finally, ensemble learning is used for secondary feature selection and classification model construction. Combing feature extraction and feature selection methods, a total of three new EEG decoding methods are obtained, namely CSP-Wavelet+LOG, CSP-WPD+LOG, and CSP-FB+LOG. Four public motor imagery datasets are used to verify the performance of the proposed methods. Compared to existing methods, the proposed methods achieved the highest average classification accuracy of 88.86, 83.40, 81.53, and 80.83 in datasets 1–4, respectively. The feature extraction time of CSP-FB is the shortest. The experimental results show that the proposed methods can effectively improve the classification accuracy and reduce the feature extraction time. With comprehensive consideration of classification accuracy and feature extraction time, CSP-FB+LOG has the best performance and can be used for the real-time BCI system.
机译：公共空间模式（CSP）是基于电机图像的大脑电脑接口（BCI）的非常有效的特征提取方法，但其性能取决于最佳频带的选择。虽然已经提出了许多研究工作来改善CSP，但大多数作品都有大的计算成本和长特征提取时间的问题。为此，本文提出了基于CSP的三种新的特征提取方法和基于非凸对数正则化的新特征选择方法。首先，EEG信号通过CSP在空间过滤，然后提出了三种新的特征提取方法。我们分别调用CSP-小波，CSP-WPD和CSP-FB。对于CSP-小波和CSP-WPD，离散小波变换（DWT）或小波分组分解（WPD）用于分解空间滤波信号，然后将小波系数的能量和标准偏差作为特征提取。对于CSP-FB，通过滤波器组（FB）将空间滤波信号滤波到多个频带中，然后将每个频带的差异的对数作为特征提取。其次，提出了用非凸对数函数正常化的稀疏优化方法，用于特征选择，我们称为日志，并给出了日志的优化算法。最后，集合学习用于辅助特征选择和分类模型构造。梳理特征提取和特征选择方法，总共获得了三种新的EEG解码方法，即CSP-WAVELET + LOG，CSP-WPD + LOG和CSP-FB +日志。四个公共电机图像数据集用于验证所提出的方法的性能。与现有方法相比，所提出的方法分别在数据集1-4中实现了88.86,83.40,81.53和80.83的最高平均分类准确度。 CSP-FB的特征提取时间是最短的。实验结果表明，该方法可以有效提高分类精度并减少特征提取时间。通过全面考虑分类准确性和特征提取时间，CSP-FB + Log具有最佳性能，可用于实时BCI系统。

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