What masking geometry works best for EEG foundation models?
EEG foundation models hold promise for scalable brain-signal decoding across clinical and cognitive neuroscience applications, yet their pre-training pipelines remain poorly understood. Among design choices, the masking strategy is particularly critical: it determines what the network must predict and from which context. Yet it has never been ablated in isolation, as each new model bundles a new masking strategy with a new backbone and objective. In this paper, we formalize the design choices for spatio-temporal masking strategies and train various models with a single pipeline under varying masking configurations across two SSL frameworks (MAE and JEPA). We then systematically evaluate t...
| Authors | Pierre Guetschel, Bruno Aristimunha, Yassine El Ouahidi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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- PierreGtch/eeg-fm-masking_mae_r9cm_L2Model · cited by
- PierreGtch/eeg-fm-masking_jepa_r9cm_L2Model · cited by
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