cs.LGFeb 28, 2026

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

Organizations: Indian Institute of Technology Mandi, India · NeuroDx, India

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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.

Figures & tables

Figure 1: PRISM architecture. Raw EEG is patchified per channel and embedded. 4D positional encoding [ 4 ] (electrode coordinates + temporal index) is added before the encoder. The decoder fills masked positions with learnable tokens and reconstructs signal. An auxiliary path pools intermediate encoder outputs via learned attention, producing a global embedding that independently reconstructs masked patches.
 CkptADFTDBCI-IV-2aHMCPhysioNetSienaEEGMAT
LPD10.5540.4810.6820.6040.6500.576
D20.5330.5070.6790.5930.6380.649
FTD10.5240.5420.7030.6310.7380.592
D20.5450.5430.6730.6400.7380.648
Table 1: Effect of pretraining population on balanced accuracy across six benchmark tasks. LP = linear probing (frozen encoder, MLP head), FT = full fine-tuning single-stage with MLP head. Bold = best, underline = second best per section-column.
HeadADFTDBCI-IV-2aHMCPhysioNetSienaEEGMAT
Attn Pool0.4850.4100.7070.6040.7370.636
Avg Pool0.5270.3890.7050.6270.7870.607
MLP0.5450.5430.6730.6400.7380.648
Table 2: Head ablation on D2, full fine-tuning. Bold = best, underline = second best per column.
StrategyADFTDBCI-IV-2aHMCPhysioNetSienaEEGMAT
LP0.5330.5070.6790.5930.6380.649
Full-Dual0.5570.5210.6960.5980.6130.635
Full-Single0.5450.5430.6730.6400.7380.648
Partial-Single0.5310.5180.6820.6070.6630.658
Table 3: Adaptation strategy comparison on D2, MLP head. Full-Single = all parameters trained end-to-end; Full-Dual = LP stage then full fine-tuning; Partial-Single = only last encoder layer unfrozen. Bold = best, underline = second best per column.
 D1D2
Full-Single0.4840.607
Table 4: Epilepsy vs. mimickers: balanced accuracy on held-out subjects. D1 = narrow-source checkpoint, D2 = multi-source checkpoint. Full-Single fine-tuning with MLP head. Bold = best, underline = second best.
 BCI-IV-2aPhysioNet-MI
ModelEEG-BenchFM-BenchEEG-BenchFM-Bench
LaBraM0.280.380.250.49
CBraMod0.310.350.270.46
REVE0.410.330.330.52
PRISM0.440.510.320.59
Table 5: Comparison with EEG foundation models under two standardized evaluation protocols on motor imagery tasks. Protocols differ in preprocessing, segmentation, checkpoint selection, and evaluation heads—see Section 4.5 for a systematic decomposition. Bold = best, underline = second best per column.
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