用环境先验信息提升脑电情绪分类，无需个体暴露数据
Neuro-Geospatial Modelling of EEG Affective States Using Literature-Informed Environmental Context
- 用脑电与环境图谱双塔模型融合分析
- 多模态准确率达76.2%，高于纯脑电的67.4%
- 适用于缺乏个体暴露数据的环境-脑电研究
空气污染和绿地等环境因素与情绪认知结果相关，但脑电与环境数据极少共地理定位。本文探究文献启发的环境先验是否可作为辅助地理空间模态，用于在缺乏个体暴露数据时进行脑电情绪状态分类。结合来自EAV基准（42名20-30岁参与者）的30通道脑电数据，以及基于OpenAQ、Sentinel-2、Sentinel-5P和OpenStreetMap的阿斯塔纳环境表征。采用双塔架构，将脑电-Conformer表示与基于图的环境编码器结合。因数据未共注册，环境上下文被视为文献启发的先验而非实测暴露。通过受试者级重复划分、置换与标签打乱控制、剂量-反应反转及域偏移实验，区分架构增益与先验依赖增益。多模态模型准确率达76.2%，优于仅使用脑电的67.4%。破坏环境-标签结构仍保留部分增益，表明提升非完全依赖环境信息。将阿斯塔纳环境分布替换为独立建模的新加坡分布后，准确率降至72.8%。结果表明技术可行性，但未建立可观测或因果暴露-情绪关联。本研究为未来联合采集移动脑电-环境数据提供框架。
原文摘要 · Abstract (English)
Environmental exposures such as air pollution and greenness have been associated with affective and cognitive outcomes, but EEG and environmental datasets are rarely jointly georeferenced. We investigate whether literature-informed environmental priors can serve as an auxiliary geospatial modality for EEG-based affective-state classification when individual-level exposure data are unavailable. We combine 30-channel EEG from the EAV benchmark (42 participants, aged 20-30 years) with environmental representations derived from OpenAQ, Sentinel-2, Sentinel-5P, and OpenStreetMap data for Astana. A dual-tower architecture combines EEG-Conformer representations with a graph-based environmental encoder. Because the datasets are not co-registered, environmental context is treated as a literature-informed prior rather than measured exposure. Subject-level repeated splits, permutation and label-shuffling controls, dose-response reversal, and domain-shift experiments distinguish architecture-level gains from prior-dependent gains. The multimodal model achieves 76.2% accuracy versus 67.4% for EEG alone. Controls disrupting environmental-label structure retain part of this gain, indicating that the improvement is not attributable solely to environmental information. Replacing the Astana environmental distribution with an independently modeled Singapore distribution reduces accuracy to 72.8%. These findings demonstrate technical feasibility but do not establish an observed or causal exposure-affect association. The study provides a framework for future jointly collected mobile EEG-environment studies. Implementation: https://github.com/r11up/geo-cog
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