Enhancing Cross-Dataset EEG Emotion Recognition: A Novel Approach With Emotional EEG Style Transfer Network

Electroencephalogram (EEG)-based emotion recognition has achieved remarkable success in both subject-dependent and subject-independent scenarios. However, overcoming the challenges associated with reduced performance in EEG emotion recognition across devices, time, space, and subjects (i.e., cross-dataset) remains a significant obstacle for affective brain-computer interfaces (aBCIs). The key issue lies in the distributional mismatch between source and target domain EEG signals. To tackle the significant inter-domain differences in cross-dataset EEG emotion recognition, this paper introduces an innovative framework termed the Emotional EEG Style Transfer Network (E$^{2}$mml:mathmml:msupmml:mrow/mml:mn2STN), which aims to effectively capture the emotional content information from the source domain and the style features from the target domain, facilitating the reconstruction of stylized emotion EEG representations. These stylized EEG representations significantly enhance the discriminative prediction performance in cross-dataset EEG emotion recognition. Specifically, E$^{2}$mml:mathmml:msupmml:mrow/mml:mn2STN consists of three key modules: a Transfer Module for domain style transfer, a Transfer Evaluation Module for evaluating transfer quality, and a Discriminative Module for making discriminative predictions. Extensive experiments demonstrate that E$^{2}$mml:mathmml:msupmml:mrow/mml:mn2STN achieves the state-of-the-art performance in cross-dataset emotion EEG recognition. To the best of our knowledge, this is the first work to explicitly address cross-dataset emotion EEG recognition. The experimental results provide a valuable benchmark for future research in this area.