  Individual differences of Electroencephalogram (EEG) could cause the domain
shift which would significantly degrade the performance of cross-subject
strategy. The domain adversarial neural networks (DANN), where the
classification loss and domain loss jointly update the parameters of feature
extractor, are adopted to deal with the domain shift. However, limited EEG data
quantity and strong individual difference are challenges for the DANN with
cumbersome feature extractor. In this work, we propose knowledge distillation
(KD) based lightweight DANN to enhance cross-subject EEG-based emotion
recognition. Specifically, the teacher model with strong context learning
ability is utilized to learn complex temporal dynamics and spatial correlations
of EEG, and robust lightweight student model is guided by the teacher model to
learn more difficult domain-invariant features. In the feature-based KD
framework, a transformer-based hierarchical temporalspatial learning model is
served as the teacher model. The student model, which is composed of Bi-LSTM
units, is a lightweight version of the teacher model. Hence, the student model
could be supervised to mimic the robust feature representations of teacher
model by leveraging complementary latent temporal features and spatial
features. In the DANN-based cross-subject emotion recognition, we combine the
obtained student model and a lightweight temporal-spatial feature interaction
module as the feature extractor. And the feature aggregation is fed to the
emotion classifier and domain classifier for domain-invariant feature learning.
To verify the effectiveness of the proposed method, we conduct the
subject-independent experiments on the public dataset DEAP with arousal and
valence classification. The outstanding performance and t-SNE visualization of
latent features verify the advantage and effectiveness of the proposed method.
