EEGTune: A data-efficient fine-tuning framework for EEG foundation models

Electroencephalography (EEG) foundation models, pre-trained on large-scale unlabeled data via self-supervised learning, have demonstrated strong generalization capabilities across various brain-computer interface (BCI) tasks. However, their practical deployment remains constrained by the expensive cost of data annotation required for task-specific fine-tuning. To mitigate this limitation, we propose EEGTune, a data-efficient fine-tuning framework for EEG foundation models that integrates active learning with consistency-based semi-supervised learning. Specifically, EEGTune first selects the most informative samples for expert annotation under a limited labeling budget and performs initial fine-tuning on this augmented annotated dataset. It then employs a consistency-based pseudo-labeling strategy, which enhances robustness to noise and eliminates manual confidence thresholding by leveraging prediction stability under stochastic augmentations. The model is finally fine-tuned on both the labeled and pseudo-labeled data, maximizing the use of all available samples. To evaluate the efficacy of EEGTune, we have conducted extensive experiments involving two state-of-the-art EEG foundation models across four representative downstream BCI tasks, including sleep staging, seizure detection, emotion recognition, and motor imagery. Experimental results demonstrate that our method achieves superior fine-tuning performance with significantly reduced labeling effort. Notably, on sleep staging and seizure detection tasks, EEGTune attains performance within 1-2% of fully supervised fine-tuning using only 15% of the labeled data. These results underscore the potential of our framework to substantially reduce annotation requirements while maintaining high performance, facilitating broader applications of EEG foundation models in real-world scenarios. The source code will be released at https://github.com/zer02her0/EEGTune .