  One of the most significant challenges of EEG-based emotion recognition is
the cross-subject EEG variations, leading to poor performance and
generalizability. This paper proposes a novel EEG-based emotion recognition
model called the domain adversarial graph attention model (DAGAM). The basic
idea is to generate a graph to model multichannel EEG signals using biological
topology. Graph theory can topologically describe and analyze relationships and
mutual dependency between channels of EEG. Then, unlike other graph
convolutional networks, self-attention pooling is applied to benefit salient
EEG feature extraction from the graph, which effectively improves the
performance. Finally, after graph pooling, the domain adversarial based on the
graph is employed to identify and handle EEG variation across subjects,
efficiently reaching good generalizability. We conduct extensive evaluations on
two benchmark datasets (SEED and SEED IV) and obtain state-of-the-art results
in subject-independent emotion recognition. Our model boosts the SEED accuracy
to 92.59% (4.69% improvement) with the lowest standard deviation of 3.21%
(2.92% decrements) and SEED IV accuracy to 80.74% (6.90% improvement) with the
lowest standard deviation of 4.14% (3.88% decrements) respectively.
