EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding

Electroencephalography (EEG) decoding models often generalize poorly across datasets and subjects due to domain shifts in acquisition protocols and individual neurophysiology. We propose EEG-PRIME, a two-stage EEG foundation model for cross-dataset multi-task decoding. EEG-PRIME combines masked pretraining with prototype-aligned instruction tuning to enable instruction-aware and subject-invariant decoding across diverse BCI paradigms. During pretraining, an EEG encoder learns transferable repres

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ethics.ai (13 August 2026), “EEG-PRIME: Prototype-Aligned Representation Learning with Multi-Level Conditioning for EEG Decoding,” evidence record 19182, https://ethics.ai/record/19182 (originally published by arXiv).

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