EEG-JEPA: Structured Latent Prediction for EEG Foundation Models
Electroencephalography (EEG) foundation models aim to learn reusable representations from large-scale unlabeled recordings. A common pretraining strategy is masked waveform reconstruction, but applying supervision directly to noisy EEG may encourage models to recover predictable background activity, acquisition effects, and artifacts rather than neural structure that transfers across tasks. This raises a central question: what should an EEG foundation model predict to learn transferable representations? We introduce EEG-JEPA a structured latent-prediction framework for EEG foundation modeling. Rather than reconstructing masked voltage samples, a masked context encoder and predictor infer ...
| Authors | Jinhao Li, Zhiyuan Ma, Xueqiao Han et al. |
|---|---|
| Primary category | eess.SP |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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