发表机构
Beijing Institute of Architectural Design Co., Ltd.; Shanghai Jiao Tong University（北京市建筑设计研究院有限公司; 上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究利用脑电图数据，通过对人工智能生成的生物数字建筑图像引发的情感反应进行研究，基于现有数据集分析进行相关操作，得出伽马和德尔塔波段准确率高，还明确关键因素与情绪的关联，为建筑设计提供了有价值的见解。
AI 中文摘要
利用人工智能生成图像的脑电图（EEG）数据，研究了对生物数字建筑的情感反应。一项有336名参与者的预实验从600张初始图像中选出60张能引发强烈情感反应的图像，分为敬畏、厌恶或满足三类。用这些图像对52名志愿者进行脑电图记录，基于现有数据集分析进行通道选择和样本量估计。伽马和德尔塔波段分类准确率最高，敬畏情绪下伽马波段准确率达77.07%±13.8%。绿化和不均匀粒度等关键因素与积极情绪有关，潮湿引发负面反应。这些结果强调了在生物数字建筑中融入自然元素和多样纹理以增强审美吸引力和接受度的重要性，证明了脑电图客观评估建筑偏好的能力，为建筑师设计有吸引力和可持续的环境提供了有价值的见解。
英文摘要
Emotional responses to biodigital architecture were examined using electroencephalographic (EEG) data from AI-generated images. A pre-experiment involving 336 participants identified 60 images, selected from an initial pool of 600, that elicited strong emotional responses categorized as awe, disgust, or content. These images were used for EEG recordings of 52 volunteers, with channel selection and sample size estimation based on the analysis of an existing dataset. Gamma and delta bands yielded the highest classification accuracy, with the gamma band achieving an accuracy of 77.07 percent +/- 13.8 percent for the awe emotion. Key factors such as greenery and non-uniform granularity were linked to positive emotions, while dampness triggered negative reactions. These results emphasize the significance of incorporating natural elements and varied textures in biodigital architecture to enhance aesthetic appeal and acceptance. The study demonstrates EEG's capability to objectively assess architectural preferences, providing valuable insights for architects to design engaging and sustainable environments.
Comments12 pages, 3 figures; published in the proceedings of SIGraDi 2024
Journal refProceedings of the 28th International Conference of the Iberoamerican Society of Digital Graphics (SIGraDi 2024): Biodigital Intelligent Systems, 2024, pp. 2443-2454
