In motor imagery brain computer interface system, the spatial covariance matrices of EEG signals which carried important discriminative information have been well used to improve the decoding performance of motor imagery. However, the covariance matrices often suffer from the problem of high dimensionality, which leads to a high computational cost and overfitting. These problems directly limit the application ability and work efficiency of the BCI system. To improve these problems and enhance the performance of the BCI system, in this study, we propose a novel semisupervised locality-preserving graph embedding model to learn a low-dimensional embedding. This approach enables a low-dimensional embedding to capture more discriminant information for classification by efficiently incorporating information from testing and training data into a Riemannian graph. Furthermore, we obtain an efficient classification algorithm using an extreme learning machine (ELM) classifier developed on the tangent space of a learned embedding. Experimental results show that our proposed approach achieves higher classification performance than benchmark methods on various datasets, including the BCI Competition IIa dataset and in-house BCI datasets.

中文翻译：

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在脑机接口系统中嵌入切线空间极限学习机进行脑电图解码

在运动意象脑机接口系统中，携带重要判别信息的脑电信号的空间协方差矩阵已被很好地用于提高运动意象的解码性能。然而，协方差矩阵往往存在高维的问题，导致计算成本高和过度拟合。这些问题直接限制了BCI系统的应用能力和工作效率。为了改善这些问题并提高 BCI 系统的性能，在本研究中，我们提出了一种新的半监督局部保留图嵌入模型来学习低维嵌入。这种方法通过将来自测试和训练数据的信息有效地合并到黎曼图中，使低维嵌入能够捕获更多判别信息以进行分类。此外，我们使用在学习嵌入的切线空间上开发的极限学习机 (ELM) 分类器获得了一种有效的分类算法。实验结果表明，我们提出的方法在各种数据集上实现了比基准方法更高的分类性能，包括 BCI 竞赛 IIa 数据集和内部 BCI 数据集。