Stefanie Liebe

Parameter-Efficient Self-Supervised Adaptation for EEG-FM under Fixed Computational Budgets

EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especially across diverse clinical datasets. Full fine-tuning is impractical for resource-constrained clinical settings due to high computational requirements. In this work, we i...