Decoding emotional responses to AI-generated architectural imagery.

Introduction: The integration of AI in architectural design represents a significant shift toward creating emotionally resonant spaces. This research investigates AI's ability to evoke specific emotional responses through architectural imagery and examines the impact of professional training on emotional interpretation.

Methods: We utilized Midjourney AI software to generate images based on direct and metaphorical prompts across two architectural settings: home interiors and museum exteriors.

Mini review: Challenges in EEG emotion recognition.

Electroencephalography (EEG) stands as a pioneering tool at the intersection of neuroscience and technology, offering unprecedented insights into human emotions. Through this comprehensive review, we explore the challenges and opportunities associated with EEG-based emotion recognition. While recent literature suggests promising high accuracy rates, these claims necessitate critical scrutiny for their authenticity and applicability.

Uncovering the connection between ceiling height and emotional reactions in art galleries with editable 360-degree VR panoramic scenes.

Introduction: This study investigates the relationship between ceiling height and emotional responses in art galleries, using editable 360-degree VR panoramic scenes. Prior research has explored the influence of spatial dimensions on general emotions, but the specific impact of ceiling height in art gallery settings, particularly on discrete emotions, remains understudied.

Methods: The study utilized 360-degree panoramic photo scene modeling to modify ceiling heights within virtual art galleries, assessing emotional responses through self-report measures.

Facial expression recognition in virtual reality environments: challenges and opportunities.

This study delved into the realm of facial emotion recognition within virtual reality (VR) environments. Using a novel system with MobileNet V2, a lightweight convolutional neural network, we tested emotion detection on 15 university students. High recognition rates were observed for emotions like "Neutral", "Happiness", "Sadness", and "Surprise".