Separating personal from population gains when calibrating EEG foundation models for new users

Abstract:Foundation models are increasingly adapted to individual users, but an apparent personalization gain can simply reflect a stronger population model. This distinction matters for brain-computer interfaces, where every new user must be calibrated. We evaluated personal adaptation of three frozen EEG foundation models (CBraMod, REVE and LaBraM) in 235 held-out subjects from three motor-imagery datasets, comparing each subject's adapter with the population model and with adapters fitted to other subjects. Using all first-half session labels, personal adapters improved mean balanced accuracy over the population model by 1.5-5.4 percentage points and outperformed exchanged adapters by 2.3-7.3 points in all nine model-dataset combinations. The size of this benefit depended on population training: with four times the original budget, median gains remained positive (1.0-2.0 points) but were smaller for every model, and no population model reached a confirmed plateau. Acquiring the benefit cheaply was unreliable: few-label calibration was consistently non-negative on only one dataset, and in CBraMod neither unlabeled context nor meta-learned initialization outperformed matched controls. Personalization should therefore be evaluated against both a population reference and exchanged parameters, across population-training budgets.

59 pages, 19 figures (6 main, 13 supplementary), 33 tables (3 main, 30 supplementary). Includes supplementary information and source data. Code: this https URL

Machine Learning (cs.LG); Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC)

arXiv:2609.34801 [cs.LG]

(or arXiv:2609.34801v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.34801 arXiv-issued DOI via DataCite (pending registration)