Abstract：
Objective. The number of electrode channels in a motor imagery-based brain-computer interface (MI-BCI) system influences not only its decoding performance, but also its convenience for use in applications. Although many channel selection methods have been proposed in the literature, they are usually based on the univariate features of a single channel. This leads to a loss of the interaction between channels and the exchange of information between networks operating at different frequency bands.Approach. We integrate brain networks containing four frequency bands into a multilayer network framework and propose a multilayer network-based channel selection (MNCS) method for MI-BCI systems. A graph learning-based method is used to estimate the multilayer network from electroencephalogram (EEG) data that are filtered by multiple frequency bands. The multilayer participation coefficient of the multilayer network is then computed to select EEG channels that do not contain redundant information. Furthermore, the common spatial pattern (CSP) method is used to extract effective features. Finally, a support vector machine classifier with a linear kernel is trained to accurately identify MI tasks.Main results. We used three publicly available datasets from the BCI Competition containing data on 12 healthy subjects and one dataset containing data on 15 stroke patients to validate the effectiveness of our proposed method. The results showed that the proposed MNCS method outperforms all channels (85.8% vs. 93.1%, 84.4% vs. 89.0%, 71.7% vs. 79.4%, and 72.7% vs. 84.0%). Moreover, it achieved significantly higher decoding accuracies on MI-BCI systems than state-of-the-art methods (pairedt-tests,p< 0.05).Significance. The experimental results showed that the proposed MNCS method can select appropriate channels to improve the decoding performance as well as the convenience of the application of MI-BCI systems.
摘要：
目的：基于运动图像的脑机接口（MI-BCI）系统中电极通道的数量不仅影响其解码性能，而且在应用中使用也很方便。尽管文献中已经提出了许多信道选择方法,它们通常基于单通道的单变量特征。这导致信道之间的交互和在不同频带操作的网络之间的信息交换的损失。＆＃xD;方法：我们将包含四个频段的大脑网络集成到多层网络框架中，并提出了一种用于MI-BCI系统的基于多层网络的信道选择（MNCS）方法。基于图形学习的方法用于从通过多个频带过滤的脑电图（EEG）数据中估计多层网络。然后计算多层网络的多层参与系数(MPC)以选择不包含冗余信息的EEG通道。此外，利用公共空间模式(CSP)方法提取有效特征。最后,训练具有线性核的支持向量机(SVM)分类器以准确识别MI任务。主要结果：我们使用了来自BCI竞赛的三个公开可用的数据集，其中包含12名健康受试者的数据和一个包含15名中风患者数据的数据集，以验证我们提出的方法的有效性。结果表明，所提出的MNCS方法优于所有通道(85.8%vs.93.1%,84.4%vs.89.0%,71.7%与79.4%,和72.7%vs.84.0%）。此外，它在MI-BCI系统上的解码精度明显高于最先进的方法(配对t检验，p<0.05)。＆＃xD;意义：实验结果表明，所提出的MNCS方法可以选择合适的通道，以提高解码性能以及MI-BCI系统应用的便利性。
