Affective brain–computer interfaces (aBCIs) have shown promising applications due to the significant advancements in utilizing electroencephalogram (EEG) signals for emotion recognition. By measuring neuronal activity across various brain regions, EEG provides rich spatial information that is essential for discriminative feature extraction and effective emotion recognition. However, existing deep learning models face challenges in effectively capturing and leveraging local and global spatial dependencies. To address this, we propose a hierarchical spatial aggregation Transformer (HSA-Former) for EEG-based emotion recognition, which explores spatial relationships from multiple levels, including electrodes, intrabrain regions, and interbrain regions. The HSA-Former is characterized by: 1) a multihierarchical spatial information learning architecture that sequentially extracts diverse spatial features from electrodes, through intrabrain regions, and across interbrain regions; 2) a parallel learning approach that captures internal spatial features of different brain regions from different channels; and 3) an effective aggregation method that mitigates the problem of information loss typically caused by direct pooling in Transformers. Extensive experiments on three public datasets (i.e., SEED, SEED-IV, and SEED-V) demonstrate that the proposed HSA-Former outperforms existing state-of-the-art methods. Interestingly, the weights learned by HSA-Former emphasize the frontal, temporal, and occipital lobes as critical brain regions, aligning with established mechanisms underlying emotion recognition.

Artificial Intelligent Generation Content (AIGC), has been widely disseminated in the fields of technology, academia, and the arts. This project explores the application of various AI tools and the visualization of dream experiences through multimedia. It utilizes AI-generated multimodal materials as perceptible dream content, employs light and mechanical installations to create immersive dream atmospheres, and employs a fictional AI-Mulan narrator to recount her dream story. Through artistic practice, it delves into Mulan's unconscious realm and conducts a psychoanalysis of a historical figure. It represents an interdisciplinary exploration of art and psychoanalysis through AI visualization.

People who are to live, study and work abroad will face more challenges in the new cultural environment and suffer more acculturative stress. Virtual Reality (VR), by which an immersive learning environment can be built, may help them adapt to a foreign culture at a lower cost of time and money. In order to work out a design method for culture learning in VR, we have designed a VR application so that learners can experience and learn the typical western festival culture – Christmas culture – in an immersive environment. To evaluate the effectiveness of the VR method, 50 EFL Chinese university students were enrolled in our experiments and randomly assigned to the VR group and the non-VR group, the data was drawn from cultural knowledge questionnaire, behavior test and Intercultural Sensitivity Scale (ISS). The ANCOVA revealed no major effect for group factor on knowledge learning. Similarly, the Mixed ANOVA identified no major effect for group factor on behavior learning and attitude learning. There was no interaction effect between time and group in all experiments. Our results show that the VR method is preferred by most of the participants, but it shows no remarkable advantage over the non-VR method. Moreover, regression analysis between the culture learning and the sense of presence in VR shows that presence has the potential to improve the performance of intercultural interaction engagement. Our findings are of practical value for culture learning in VR.