Hu Haochen, Yue Kang, Guo Mei, Lu Kai, Liu Yue
Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
Institute of Software, Chinese Academy of Sciences, Beijing 100045, China.
Brain Sci. 2023 Jan 28;13(2):221. doi: 10.3390/brainsci13020221.
Motor imagery brain-computer interface (MI-based BCIs) have demonstrated great potential in various applications. However, to well generalize classifiers to new subjects, a time-consuming calibration process is necessary due to high inter-subject variabilities of EEG signals. This process is costly and tedious, hindering the further expansion of MI-based BCIs outside of the laboratory. To reduce the calibration time of MI-based BCIs, we propose a novel domain adaptation framework that adapts multiple source subjects' labeled data to the unseen trials of target subjects. Firstly, we train one Subject Separation Network(SSN) for each of the source subjects in the dataset. Based on adversarial domain adaptation, a shared encoder is constructed to learn similar representations for both domains. Secondly, to model the factors that cause subject variabilities and eliminate the correlated noise existing in common feature space, private feature spaces orthogonal to the shared counterpart are learned for each subject. We use a shared decoder to validate that the model is actually learning from task-relevant neurophysiological information. At last, an ensemble classifier is built by the integration of the SSNs using the information extracted from each subject's task-relevant characteristics. To quantify the efficacy of the framework, we analyze the accuracy-calibration cost trade-off in MI-based BCIs, and theoretically guarantee a generalization bound on the target error. Visualizations of the transformed features illustrate the effectiveness of domain adaptation. The experimental results on the BCI Competition IV-IIa dataset prove the effectiveness of the proposed framework compared with multiple classification methods. We infer from our results that users could learn to control MI-based BCIs without a heavy calibration process. Our study further shows how to design and train Neural Networks to decode task-related information from different subjects and highlights the potential of deep learning methods for inter-subject EEG decoding.
运动想象脑机接口（基于MI的BCI）在各种应用中已展现出巨大潜力。然而，由于脑电图（EEG）信号在不同受试者之间存在高度变异性，为了将分类器很好地推广到新受试者，需要一个耗时的校准过程。这个过程既昂贵又繁琐，阻碍了基于MI的BCI在实验室之外的进一步扩展。为了减少基于MI的BCI的校准时间，我们提出了一种新颖的域适应框架，该框架将多个源受试者的标记数据适应于目标受试者的未见过的试验。首先，我们为数据集中的每个源受试者训练一个受试者分离网络（SSN）。基于对抗域适应，构建一个共享编码器以学习两个域的相似表示。其次，为了对导致受试者变异性的因素进行建模并消除公共特征空间中存在的相关噪声，为每个受试者学习与共享特征空间正交的私有特征空间。我们使用一个共享解码器来验证模型实际上是从与任务相关的神经生理信息中学习。最后，通过使用从每个受试者的任务相关特征中提取的信息对SSN进行集成，构建一个集成分类器。为了量化该框架的有效性，我们分析了基于MI的BCI中的准确率 - 校准成本权衡，并从理论上保证了目标误差的泛化界。变换后特征的可视化说明了域适应的有效性。在BCI竞赛IV - IIa数据集上的实验结果证明了所提出框架与多种分类方法相比的有效性。我们从结果中推断，用户可以在无需繁重校准过程的情况下学会控制基于MI的BCI。我们的研究进一步展示了如何设计和训练神经网络以从不同受试者解码与任务相关的信息，并突出了深度学习方法在受试者间EEG解码方面的潜力。
