Motor imagery (MI) is an important brain-computer interface (BCI) paradigm, which can be applied without external stimulus. Imagining different joint movements from the same limb allows intuitive control of the outer devices. However, few researches focused on this field, and the decoding accuracy limited the applications for practical use. In this study, we aim to use deep learning methods to explore the ceiling of the decoding performance of three tasks: the resting state, the MI of right hand and right elbow. To represent the brain functional relationships, the correlation matrix that consists of correlation coefficients between electrodes (channels) was calculated as features. We proposed the Channel-Correlation Network to learn the overall representation among channels for classification. Ensemble learning was applied to integrate the output of multiple Channel-Correlation Networks. Our proposed method achieved the decoding accuracy of up to 87.03% in the 3-class scenario. The results demonstrated the effectiveness of deep learning method for decoding MI of different joints from the same limb and the potential of this fine paradigm to be applied in practice.

中文翻译：

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用于从同一肢体解码运动图像的深通道相关网络。

运动图像（MI）是重要的脑机接口（BCI）范例，无需外部刺激即可应用。想象来自同一肢体的不同关节运动可以直观地控制外部设备。但是，针对该领域的研究很少，并且解码精度限制了其实际应用。在这项研究中，我们旨在使用深度学习方法来探索三个任务的解码性能的上限：静止状态，右手和右肘的MI。为了表示脑功能关系，将由电极（通道）之间的相关系数组成的相关矩阵计算为特征。我们提出了通道相关网络，以学习分类通道之间的整体表示。集成学习用于集成多个通道相关网络的输出。我们提出的方法在三类情况下实现了高达87.03％的解码精度。结果证明了深度学习方法对同一肢体不同关节的MI解码的有效性以及这种精细范例在实践中的应用潜力。