将脑电图模型的预测解释转化为频域和源空间，更贴近临床理解。
EEG-PRISM: Physiologically-Grounded Interpretability of Predictions by EEG Foundation Models
- 通过线性变换与反向传播，将时间-通道归因映射到频域和脑源域。
- 模拟数据中频域恢复接近完美，癫痫患者定位发作区准确率达50%。
- 适用于癫痫和自闭症研究，可发现临床相关的生物标志物。
目标：基础模型代表脑电图（EEG）分析的下一代AI；然而现有可解释AI技术仅提供时间-通道输入空间的归因分数，与临床对EEG的理解不一致。因此亟需一种通用方法，可在不修改或重新训练底层模型的前提下，将任何基础模型的可解释性扩展至其他生理相关领域。方法：EEG-PRISM利用线性变换和标准反向传播规则，将时间-通道归因分数映射至替代域。通过可逆DFT实现频域映射，通过近似可逆的EEG生成模型实现源域映射。我们在模拟与真实数据上评估EEG-PRISM，使用五种基础模型和四种可解释AI工具，评估不同域中对真实现象的恢复效果。结果：在模拟中，EEG-PRISM实现近乎完美的频域恢复，空间定位准确率为69.2%。在癫痫研究中，正确识别出δ-θ活动最显著，并在50%的病例中准确定位发作起始区域。在自闭症研究中，成功将预测性δ-α生物标志物定位至额叶和颞叶，与既往研究一致。结论：EEG-PRISM是一种理论基础坚实的后处理归因方法，能准确映射至频域和空间域，支持瞬时事件（如癫痫发作）的窗口级分析及临床相关生物标志物的群体级识别，推动可解释性EEG基础模型的发展。意义：该工作实现了对EEG基础模型的生理学根基解释，支持事件定位与生物标志物识别等临床洞察。
原文摘要 · Abstract (English)
Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scores in the time-channel input space, which is mismatched to clinical intuition about EEG. Thus, there is a critical need for a universal method that can extend the interpretability of any foundation model to alternative and physiologically relevant domains without modifying or retraining the underlying model. Methods: EEG-PRISM leverages linear transformations and established backpropagation rules to map time-channel attribution scores into alternative domains. We derive mappings to the frequency domain via an invertible DFT and to the source domain via an approximately invertible EEG generative model. We evaluate EEG-PRISM in simulated and real data, assessing recovery of ground-truth phenomena across domains with five foundation models and four AI explainers. Results: In simulation, EEG-PRISM achieves near-perfect spectral recovery and 69.2% spatial accuracy. In epilepsy, EEG-PRISM correctly determines that delta-theta activity is most salient and correctly localizes the seizure onset region with 50% accuracy. In autism, EEG-PRISM localizes the predictive delta-alpha biomarkers to frontal and temporal regions, consistent with prior work. Conclusion: EEG-PRISM is a theoretically-grounded post-hoc attribution method with accurate mapping into the spectral and spatial domains. It supports window-level analysis of transient events (e.g., seizures) and group-level identification of clinically relevant biomarkers (e.g., autism), thus advancing interpretable EEG foundation models. Significance: This work enables physiologically-grounded interpretation of EEG foundation models and supports clinically relevant insights such as event localization and biomarker identification.
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