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.
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Publication details
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- Open access
- Green open access
Cite this article
APA 7
Tao, X., & Chen, K. (2026). Separating personal from population gains when calibrating EEG foundation models for new users. https://omanscience.com/en/articles/separating-personal-from-population-gains-when-calibrating-eeg-foundation-models-for-new-users
MLA 9
Tao, Xilin, and Kani Chen. "Separating personal from population gains when calibrating EEG foundation models for new users." https://omanscience.com/en/articles/separating-personal-from-population-gains-when-calibrating-eeg-foundation-models-for-new-users.
Chicago (author–date)
Tao, Xilin, and Kani Chen. 2026. "Separating personal from population gains when calibrating EEG foundation models for new users." https://omanscience.com/en/articles/separating-personal-from-population-gains-when-calibrating-eeg-foundation-models-for-new-users.
Harvard
Tao, X. and Chen, K. (2026) 'Separating personal from population gains when calibrating EEG foundation models for new users', Available at: https://omanscience.com/en/articles/separating-personal-from-population-gains-when-calibrating-eeg-foundation-models-for-new-users.
Vancouver
Tao X, Chen K. Separating personal from population gains when calibrating EEG foundation models for new users. https://omanscience.com/en/articles/separating-personal-from-population-gains-when-calibrating-eeg-foundation-models-for-new-users
IEEE
X. Tao, and K. Chen, "Separating personal from population gains when calibrating EEG foundation models for new users," https://omanscience.com/en/articles/separating-personal-from-population-gains-when-calibrating-eeg-foundation-models-for-new-users.