Predicting Only from Selected Evidence: A Tempered Product-of-Experts Bottleneck for Auditable EEG Diagnosis

Pretrained EEG backbones improve transfer performance, but downstream diagnostic heads remain difficult to audit: predictions are typically computed from unconstrained hidden representations, while explanations are often generated only post hoc. We introduce tPoE-EIB, an $\textit{evidence-information bottleneck}$ head for adapting EEG backbones under an evidence-only prediction constraint. tPoE-EIB selects temporal and channel-specific evidence, maps the resulting summaries to Gaussian experts over a shared latent variable, and fuses them via a tempered product-of-experts posterior. The classifier conditions exclusively on this latent variable, yielding an explicit decision pathway whose information flow is constrained by the expected posterior KL divergence. This formulation induces a tractable supervised objective with an information-rate penalty, while the closed-form tempered posterior mitigates overconfident aggregation of correlated evidence sources. We evaluate tPoE-EIB on pretrained EEG foundation model backbones across six diagnostic tasks: event-type classification, abnormality detection, seizure detection, cognitive decline staging, depression screening, and cerebrovascular disease classification. The evaluation spans public benchmarks and in-house clinical cohorts, covering both binary screening and fine-grained staging, as well as sparse and dense electrode montages. tPoE-EIB maintains competitive balanced accuracy while outperforming representative post-hoc explanation methods on selection-faithfulness audits, including insertion–deletion and gate-causality tests. Its structured posterior further supports integration-faithfulness evaluations, including expert-drop, posterior-reliance, and expert-disagreement tests. Overall, these results suggest that evidence-only, rate-limited fusion is a practical approach to building auditable diagnostic models on top of frozen EEG foundation backbones.