MART: Structured Regional Adaptation for EEG Representation Models

Adapting EEG representation models to downstream tasks is often treated as a parameter-efficient update problem over the representation interface exposed by the backbone. We argue that, for EEG, the interface itself can be a limiting factor because flat hidden states may obscure the channel-time provenance and montage-dependent measurement conditions from which EEG representations are derived. We propose MART, a Measurement-Aware Regional Transformation framework that transforms this exposed interface into a structured regional adaptation space before lightweight downstream adaptation. Given EEG representations with channel-time provenance, MART recovers their channel-time organization, reparameterizes them into scalp-region representations, and augments each region with coverage and availability descriptors. A lightweight residual regional adapter and a linear prediction head are then optimized in this structured space. Across four downstream EEG tasks, MART achieves the best average Macro-F1 in our controlled adaptation comparison while updating only 0.108%–0.501% of the model parameters. Overall, MART reframes EEG representation adaptation as a problem of not only how to update a representation model, but also how its representations should be exposed to downstream adaptation.