Locally temporal-spatial pattern learning with graph attention mechanism for EEG-based emotion recognition
This work addresses emotion recognition for affective computing applications, but it appears incremental as it builds on graph attention mechanisms with added components for robustness.
The paper tackled the problem of emotion recognition from EEG signals, which fluctuate over time and have complex spatial topology, by proposing a locally temporal-spatial pattern learning graph attention network (LTS-GAT) that achieved demonstrated effectiveness compared to existing methods on two public datasets.
Technique of emotion recognition enables computers to classify human affective states into discrete categories. However, the emotion may fluctuate instead of maintaining a stable state even within a short time interval. There is also a difficulty to take the full use of the EEG spatial distribution due to its 3-D topology structure. To tackle the above issues, we proposed a locally temporal-spatial pattern learning graph attention network (LTS-GAT) in the present study. In the LTS-GAT, a divide-and-conquer scheme was used to examine local information on temporal and spatial dimensions of EEG patterns based on the graph attention mechanism. A dynamical domain discriminator was added to improve the robustness against inter-individual variations of the EEG statistics to learn robust EEG feature representations across different participants. We evaluated the LTS-GAT on two public datasets for affective computing studies under individual-dependent and independent paradigms. The effectiveness of LTS-GAT model was demonstrated when compared to other existing mainstream methods. Moreover, visualization methods were used to illustrate the relations of different brain regions and emotion recognition. Meanwhile, the weights of different time segments were also visualized to investigate emotion sparsity problems.