From Representation to Competence: What Is Missing in EEG Foundation Models?

Electroencephalography (EEG) foundation models are pretrained for broad transfer, yet their downstream performance remains uneven. Adding physiological descriptors improves prediction from frozen classifier features, and the same descriptor families are recoverable across the evaluated backbones. How much additional task performance can be recovered from these representations has remained unclear. At several internal locations, readout of the descriptor families is stronger than at the classifier input. A low rank residual adapter is therefore learned within the backbone from task labels, with the pretrained weights and classifier kept fixed. The preferred update depth varies across models and tasks, and task validation yields performance close to that of the better fixed placement. Across four backbones and four tasks, adaptation improves performance in most settings with only a few thousand trainable parameters. At the same budget, internal updates outperform updates at the structured final output in most paired comparisons. These results support placing a small residual adapter inside the backbone at a depth selected by task validation.