EEG-PRISM：基于生理学的脑电基础模型预测可解释性方法

中文摘要 AI 辅助

目标：基础模型是脑电（EEG）分析领域人工智能的下一次进步；然而当前可解释人工智能技术在时间-通道输入空间中提供归因分数，这与临床对脑电的直觉认知不匹配。因此亟需一种通用方法，可在不修改或重新训练基础模型的前提下，将任何基础模型的可解释性扩展至其他与生理学相关的领域。方法：EEG-PRISM利用线性变换和已确立的反向传播规则，将时间-通道归因分数映射至其他领域。我们通过可逆离散傅里叶变换（DFT）推导至频率域的映射，通过近似可逆的脑电生成模型推导至源域的映射。我们在模拟数据和真实数据中评估EEG-PRISM，使用5种基础模型和4种人工智能解释器评估其在各领域对真实现象的恢复能力。结果：在模拟实验中，EEG-PRISM实现近乎完美的频谱恢复和69.2%的空间准确率；在癫痫研究中，EEG-PRISM正确确定δ-θ活动最为显著，且以50%的准确率正确定位癫痫发作起始区；在自闭症研究中，EEG-PRISM将预测性δ-α生物标志物定位至额叶和颞叶区域，与先前研究结果一致。结论：EEG-PRISM是一种基于理论的事后归因方法，可准确映射至频谱和空间域，支持瞬态事件（如癫痫发作）的窗口级分析及临床相关生物标志物（如自闭症）的组级识别，从而推进可解释脑电基础模型的发展。意义：本研究实现了基于生理学的脑电基础模型解释，并支持临床相关见解，如事件定位和生物标志物识别。

英文摘要

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.

发表机构

• Johns Hopkins University（约翰斯·霍普金斯大学）
• Boston University（波士顿大学）

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