CoEEG: Benchmarking Collaborative Learning in EEG Decoding

Electroencephalography (EEG) decoding is constrained by between-subject variability, scarce per-user labeled data, and privacy restrictions that scatter records across clinics and devices. In this work, we build CoEEG to jointly compare model families and collaborative learning protocols on three EEG paradigms, Motor Imagery (MI), SSVEP, and P300. Our benchmark covers three model families, traditional machine learning with paradigm-specific priors, deep models trained from scratch, and EEG foundation models. We compare ten widely used models under four protocols that span non-collaborative and collaborative training. We find that collaboration helps EEG decoding, but not uniformly. On SSVEP, nine of ten models benefit, consistent with a decision boundary that is largely shared across subjects. On P300, every model benefits only when each client keeps its classification head private. On MI, the gains concentrate in models that perform poorly on their own. Across the six federated cells the from-scratch family has the higher family mean, though a foundation model posts the single best score in two of them. The sharing boundary follows a measured factor, how far representations align, and an inferred one, whether the decision boundary is shared. A private head splits motor imagery and never lifts the weakest clients, who lose 20.7 percentage points (pp) on SSVEP against 9.3 pp for the strongest. Once parameter count, memory, and communication are included, lightweight models are the better performance–cost choice in most settings, and the largest foundation model transmits up to 378.73 GiB. Because the value of collaborative learning depends jointly on the (1) paradigm, (2) model, and (3) sharing boundary, CoEEG supports holistic evaluation and model selection for different EEG decoding tasks.