  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.
