基于文献先验环境上下文的脑电情感状态神经地理空间建模

中文摘要 AI 辅助

空气污染和绿地等环境暴露与情感及认知结果存在关联，但脑电（EEG）与环境数据集很少进行联合地理配准。本研究在个体水平暴露数据不可用时，探究文献先验环境先验能否作为基于EEG的情感状态分类的辅助地理空间模态。我们将来自EAV基准（42名年龄20-30岁的参与者）的30通道EEG，与来自OpenAQ、Sentinel-2、Sentinel-5P及OpenStreetMap数据的阿斯塔纳环境表示相结合，采用双塔式架构融合EEG-Conformer表示与基于图的环境编码器。由于数据集未共同配准，环境上下文被视为文献先验而非实测暴露。通过受试者水平重复拆分、置换与标签打乱控制、剂量反应反转及域偏移实验，区分架构级增益与依赖先验的增益。该多模态模型准确率达76.2%，而仅用EEG的准确率为67.4%。破坏环境标签结构的控制实验保留了部分增益，表明改进并非仅归因于环境信息；将阿斯塔纳环境分布替换为独立建模的新加坡分布，准确率降至72.8%。这些发现证明了技术可行性，但未确立观察到的或因果性的暴露-情感关联，本研究为未来联合采集移动EEG-环境研究提供了框架，实现代码见this https URL。

英文摘要

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

发表机构

• The University of Melbourne（墨尔本大学）

机构由 AI 辅助整理，请以论文原文为准。