用脑电图分析AI生成建筑图像引发的情绪反应。
EEG Emotion Recognition From AI-Generated Biodigital Architecture Images
- 通过脑电数据识别观众对AI生成建筑图像的情绪。
- γ波段对敬畏情绪识别准确率达77.07%±13.8%。
- 绿色元素与不均匀纹理促进正向情绪，潮湿感引发负面反应。
采用脑电图（EEG）数据研究了人们对AI生成的生物数字建筑图像的情绪反应。前期336名参与者筛选出60张能引发强烈情绪反应的图像，从初始600张中选出，情绪类别包括敬畏、厌恶和满足。使用这60张图像对52名志愿者进行EEG记录，基于现有数据集分析进行通道选择和样本量估算。γ波段和δ波段分类准确率最高，其中γ波段对敬畏情绪的识别准确率为77.07%±13.8%。关键因素如绿化程度和非均匀纹理与积极情绪相关，而潮湿感则引发负面反应。结果表明，在生物数字建筑中融入自然元素与多样纹理可提升审美吸引力与接受度。本研究展示了EEG在客观评估建筑偏好方面的潜力，为建筑师设计更具吸引力与可持续性的环境提供了重要参考。
原文摘要 · Abstract (English)
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.
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