Electroencephalography (EEG) emotion recognition plays a key role in improving human-machine interactions. Advanced algorithms have been proposed for this task. However, two challenges remain, i.e., unclear decision boundary in the embedded space and noise in physiological signals from various devices. To this end, we develop a novel framework, namely, UACL-Net, for EEG emotion recognition. It is based on uncertainty-aware contrastive learning (UACL) and frequency-aware self-attention (FASA). Specifically, UACL uses a multivariate Gaussian distribution to construct the latent space for different emotions. It is able to highlight interclass differences, thereby improving the robustness of model decisions. In addition, FASA generates learnable weights by applying self-attention (SA) to the real and imaginary components in the frequency domain. This helps adaptively reduce noise and capture global dependencies in temporal sequences. Our model is trained and tested on four benchmark datasets, achieving up to 94.88%, 98.71%, 96.91%, and 99.29% accuracy on SEED, DEAP, DREAMER, and FACED, respectively. Experimental results demonstrate that it is effective and has advantages over peer state-of-the-art (SOTA) methods.
Xu et al. (Thu,) studied this question.