Abstract:Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for downstream deployment. We present the first systematic comparison of four channel adaptation methods (Conv1d projection, spherical spline interpolation (SSI), source-space decomposition, and Riemannian re-centering) across five pretrained EEG foundation models (5M--157M parameters), five downstream tasks, and two training regimes with 10--15 random seeds each. We find that rigid-montage models (BENDR, Neuro-GPT) require external adaptation, while flexible models (EEGPT, CBraMod) match or exceed it natively when fine-tuned but benefit from external methods under frozen-encoder deployment. A probe-SFT asymmetry exists: external adaptation can cause severe negative transfer during fine-tuning of flexible models. The optimal method is architecture-dependent (Conv1d for BENDR, SSI/Riemannian for Neuro-GPT, source-space decomposition for depression detection), and 5M-parameter CBraMod outperforms models up to 31$\times$ larger on 4/5 datasets, consistent with independent findings that compact EEG-specific architectures can match larger models.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.23091 [cs.LG] |
| (or arXiv:2604.23091v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.23091 arXiv-issued DOI via DataCite (pending registration) |
From: Kuntal Kokate [view email]
[v1]
Sat, 25 Apr 2026 01:10:40 UTC (517 KB)
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