Emotion recognition is a key attribute for realizing advances in human–computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results.

Demographic change confronts us with an ever-increasing number of elderly people who face isolation and socialization issues. Background: The main challenge of this study is to inject emotional and aesthetic aspects into the design process of a virtual reality (VR) social space for the elderly. In this context, we asked architects and artists to improve the perception elderly people have of their way of communicating with others. Artists, in collaboration with computer engineers, designed experiences that evoke positive cognitive and emotional feelings and memories by following design trends and aesthetic values likely to be appreciated by older people, which were integrated in VR. Methods: We approached our goal by implementing an innovative art-driven methodology, using a plethora of technologies and methods, such as VR, artificial intelligence algorithms, visual analysis, and 3D mapping, in order to make design decisions based on a detailed understanding of the users’ preferences and collective behavior. Results: A so-called virtual village “Cap de Ballon” was co-created, having a public space inspired by the villages of Santorini and Meteora and a private space inspired by the 3D scanning of an elderly person’s apartment. Conclusions: The overall concept of the VR village‘s utility, design, and interior design were appreciated by the end users and the concept was evaluated as original and stimulating for creativity.

The intersection of serious games, advanced technologies, and culture offers new paths for fostering social inclusion and sustainability. This position paper presents i-Game, an EU project dedicated to transforming the relationship between cultural heritage and the gaming industry. By creating an open-source platform for inclusive game development, i-Game integrates tools such as Explainable AI (XAI) and virtual reality to enable diverse stakeholders—including underrepresented groups—to co-create games that address societal and cultural challenges. Through pilot projects in museums and the textile industry, i-Game explores the potential of gamification to enhance learning, creativity, and collaboration while advancing sustainable practices. The project highlights how interdisciplinary methodologies and ethical-design principles can reshape the cultural heritage landscape, democratizing access to innovative tools and fostering a deeper connection between technology and heritage.

Seniors, in order to be able to fight loneliness, need to communicate with other people and be engaged in activities to keep their minds active to increase their social capital. There is an intensified interest in the development of social virtual reality environments, either by commerce or by academia, to address the problem of social isolation of older people. Due to the vulnerability of the social group involved in this field of research, the need for the application of evaluation methods regarding the proposed VR environments becomes even more important. The range of techniques that can be exploited in this field is constantly expanding, with visual sentiment analysis being a characteristic example. In this study, we introduce the use of image-based sentiment analysis and behavioural analysis as a technique to assess a social VR space for elders and present some promising preliminary results.

As artificial intelligence (AI) systems become increasingly integrated into game design, the demand for transparent and adaptive decision-making grows. While Explainable AI (XAI) has illuminated the internal reasoning of AI agents, most explanation-based training methods have traditionally prioritized alignment with a teacher model over the exploration of strategic diversity. In this paper, we introduce a novel framework that leverages explanation-based knowledge distillation to modulate agents’ internal reasoning, yielding both convergent and divergent behavioral strategies. To demonstrate this approach, we conducted experiments in a Tetris environment comparing baseline agents trained with standard reinforcement learning to agents whose training was modified by incorporating explainability losses. Our dynamic framework integrates a feedback mechanism that adjusts the influence of the explainability term based on performance and strategic utility. This work demonstrates the potential of employing explainability not only as an interpretative tool but also as a means to actively diversify and refine strategies in complex, dynamic environments.