  Emotion decoding using Electroencephalography (EEG)-based affective
brain-computer interfaces represents a significant area within the field of
affective computing. In the present study, we propose a novel non-deep transfer
learning method, termed as Manifold-based Domain adaptation with Dynamic
Distribution (MDDD). The proposed MDDD includes four main modules: manifold
feature transformation, dynamic distribution alignment, classifier learning,
and ensemble learning. The data undergoes a transformation onto an optimal
Grassmann manifold space, enabling dynamic alignment of the source and target
domains. This process prioritizes both marginal and conditional distributions
according to their significance, ensuring enhanced adaptation efficiency across
various types of data. In the classifier learning, the principle of structural
risk minimization is integrated to develop robust classification models. This
is complemented by dynamic distribution alignment, which refines the classifier
iteratively. Additionally, the ensemble learning module aggregates the
classifiers obtained at different stages of the optimization process, which
leverages the diversity of the classifiers to enhance the overall prediction
accuracy. The experimental results indicate that MDDD outperforms traditional
non-deep learning methods, achieving an average improvement of 3.54%, and is
comparable to deep learning methods. This suggests that MDDD could be a
promising method for enhancing the utility and applicability of aBCIs in
real-world scenarios.
