Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315100, China.
School of Information Science and Engineering, Zhejiang University Ningbo Institute of Technology, Ningbo 315100, China.
Biomed Res Int. 2020 Feb 23;2020:7285057. doi: 10.1155/2020/7285057. eCollection 2020.
Motor imagery brain-computer interfaces (BCIs) have demonstrated great potential and attract world-spread attentions. Due to the nonstationary character of the motor imagery signals, costly and boring calibration sessions must be proceeded before use. This prevents them from going into our realistic life. In this paper, the source subject's data are explored to perform calibration for target subjects. Model trained on source subjects is transferred to work for target subjects, in which the critical problem to handle is the distribution shift. It is found that the performance of classification would be bad when only the marginal distributions of source and target are made closer, since the discriminative directions of the source and target domains may still be much different. In order to solve the problem, our idea comes that joint distribution adaptation is indispensable. It makes the classifier trained in the source domain perform well in the target domain. Specifically, a measure for joint distribution discrepancy (JDD) between the source and target is proposed. Experiments demonstrate that it can align source and target data according to the class they belong to. It has a direct relationship with classification accuracy and works well for transferring. Secondly, a deep neural network with joint distribution matching for zero-training motor imagery BCI is proposed. It explores both marginal and joint distribution adaptation to alleviate distribution discrepancy across subjects and obtain effective and generalized features in an aligned common space. Visualizations of intermediate layers illustrate how and why the network works well. Experiments on the two datasets prove the effectiveness and strength compared to outstanding counterparts.
运动想象脑机接口（BCI）已经展现出了巨大的潜力，吸引了全球的关注。由于运动想象信号的非平稳特性，在使用之前必须进行昂贵且乏味的校准过程。这使得它们无法进入我们的现实生活。在本文中，我们探索了源主体的数据，以便为目标主体进行校准。在源主体上训练的模型被转移到目标主体上使用，其中关键问题是处理分布转移。研究发现，当仅使源和目标的边缘分布更接近时，分类性能可能会很差，因为源域和目标域的判别方向可能仍然有很大的不同。为了解决这个问题，我们的想法是联合分布自适应是必不可少的。它使得在源域中训练的分类器在目标域中表现良好。具体来说，我们提出了一种用于源和目标之间联合分布差异（JDD）的度量方法。实验表明，它可以根据所属类别对齐源和目标数据。它与分类精度有直接关系，并且在转移方面效果良好。其次，我们提出了一种用于零训练运动想象 BCI 的具有联合分布匹配的深度神经网络。它同时探索了边缘和联合分布自适应，以减轻跨主体的分布差异，并在对齐的公共空间中获得有效的、通用的特征。中间层的可视化说明了网络是如何以及为何能够很好地工作的。在两个数据集上的实验证明了与优秀对手相比的有效性和优势。
