Motor imagery (MI) is a common brain computer interface (BCI) paradigm. EEG is non-stationary with low signal-to-noise, classifying motor imagery tasks of the same participant from different EEG recording sessions is generally challenging, as EEG data distribution may vary tremendously among different acquisition sessions. Although it is intuitive to consider the cross-session MI classification as a domain adaptation problem, the rationale and feasible approach is not elucidated. In this paper, we propose a Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory. The proposed framework can be easily applied to most existing artificial neural networks without altering the network structure, which facilitates our method with great flexibility and transferability. In the proposed framework, domain invariants were firstly constructed jointly with channel normalization and Euclidean alignment. Then, embedding features from source and target domain were mapped into the Reproducing Kernel Hilbert Space (RKHS) and aligned accordingly. A cosine-based center loss was also integrated into the framework to improve the generalizability of the SDDA. The proposed framework was validated with two classic and popular convolutional neural networks from BCI research field (EEGNet and ConvNet) in two MI-EEG public datasets (BCI Competition IV IIA, IIB). Compared to the vanilla EEGNet and ConvNet, the proposed SDDA framework was able to boost the MI classification accuracy by 15.2%, 10.2% respectively in IIA dataset, and 5.5%, 4.2% in IIB dataset. The final MI classification accuracy reached 82.01% in IIA dataset and 87.52% in IIB, which outperformed the state-of-the-art methods in the literature.

翻译：运动想象（MI）是脑机接口（BCI）中常见的范式。EEG信号具有非平稳性和低信噪比特性，由于不同采集session间EEG数据分布可能存在显著差异，对同一被试跨session的运动想象任务分类通常具有挑战性。虽然将跨session MI分类视为域自适应问题具有直观合理性，但其理论基础与可行方法尚未阐明。本文基于域自适应理论的数学模型，提出了一种用于跨session MI分类的孪生深度域自适应（SDDA）框架。该框架可便捷地应用于现有大多数人工神经网络而无需改变网络结构，使得该方法具有优异的灵活性和可迁移性。在提出的框架中，首先通过通道归一化与欧几里得对齐联合构建域不变量，然后将源域和目标域的嵌入特征映射到再生核希尔伯特空间（RKHS）并进行对齐。此外，框架中集成了余弦中心损失函数以提升SDDA的泛化能力。我们在两个MI-EEG公开数据集（BCI Competition IV IIA、IIB）上，结合BCI研究领域两种经典且流行的卷积神经网络（EEGNet和ConvNet）验证了所提框架。与原始EEGNet和ConvNet相比，所提SDDA框架在IIA数据集上分别将MI分类准确率提升了15.2%和10.2%，在IIB数据集上分别提升了5.5%和4.2%。最终在IIA数据集上达到82.01%的MI分类准确率，在IIB数据集上达到87.52%，优于文献中现有最优方法。