  Although deep learning-based algorithms have demonstrated excellent
performance in automated emotion recognition via electroencephalogram (EEG)
signals, variations across brain signal patterns of individuals can diminish
the model's effectiveness when applied across different subjects. While
transfer learning techniques have exhibited promising outcomes, they still
encounter challenges related to inadequate feature representations and may
overlook the fact that source subjects themselves can possess distinct
characteristics. In this work, we propose a multi-source domain adaptation
approach with a transformer-based feature generator (MSDA-TF) designed to
leverage information from multiple sources. The proposed feature generator
retains convolutional layers to capture shallow spatial, temporal, and spectral
EEG data representations, while self-attention mechanisms extract global
dependencies within these features. During the adaptation process, we group the
source subjects based on correlation values and aim to align the moments of the
target subject with each source as well as within the sources. MSDA-TF is
validated on the SEED dataset and is shown to yield promising results.
