PRISM: Exploring Heterogeneous Pretrained EEG Foundation Model Transfer to Clinical Differential Diagnosis

• URL: http://arxiv.org/abs/2603.02268v1
• Date: Sat, 28 Feb 2026 19:50:28 GMT
• Title: PRISM: Exploring Heterogeneous Pretrained EEG Foundation Model Transfer to Clinical Differential Diagnosis
• Authors: Jeet Bandhu Lahiri, Parshva Runwal, Arvasu Kulkarni, Mahir Jain, Aditya Ray Mishra, Siddharth Panwar, Sandeep Singh,
• Abstract summary: We introduce PRISM, a masked autoencoder ablated along two axes -- pretraining population and downstream adaptation.<n>We compare a narrow-source EU/US corpus against a geographically diverse pool augmented with multi-center South Asian clinical recordings.<n> PRISM matches or outperforms REVE (92 datasets, 60,000+ hours) on the majority of tasks.
• Score: 5.616707402426108
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: EEG foundation models are typically pretrained on narrow-source clinical archives and evaluated on benchmarks from the same ecosystem, leaving unclear whether representations encode neural physiology or recording-distribution artifacts. We introduce PRISM (Population Representative Invariant Signal Model), a masked autoencoder ablated along two axes -- pretraining population and downstream adaptation -- with architecture and preprocessing fixed. We compare a narrow-source EU/US corpus (TUH + PhysioNet) against a geographically diverse pool augmented with multi-center South Asian clinical recordings across multiple EEG systems. Three findings emerge. First, narrow-source pretraining yields stronger linear probes on distribution-matched benchmarks, while diverse pretraining produces more adaptable representations under fine-tuning -- a trade-off invisible under single-protocol evaluation. Trained on three source corpora, PRISM matches or outperforms REVE (92 datasets, 60,000+ hours) on the majority of tasks, demonstrating that targeted diversity can substitute for indiscriminate scale and that dataset count is a confounding variable in model comparison. Second, on a clinically challenging and previously untested task -- distinguishing epilepsy from diagnostic mimickers via interictal EEG -- the diverse checkpoint outperforms the narrow-source checkpoint by +12.3 pp balanced accuracy, the largest gap across all evaluations. Third, systematic inconsistencies between EEG-Bench and EEG-FM-Bench reverse model rankings on identical datasets by up to 24 pp; we identify six concrete sources including split construction, checkpoint selection, segment length, and normalization, showing these factors compound non-additively.

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