Standing on the Shoulders of Giants: Rethinking EEG Foundation Model Pretraining via Multi-Teacher Distillation
arXiv:2603.04478v1 Announce Type: new Abstract: Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely adapted from and inspired by the success of vision and language foundation models. However, unlike images and text, EEG datasets are notoriously expensive to collect and characterized by low signal-to-noise ratio. These challenges introduce difficulties in scaling the EEG foundation models and capturing the underlying neural semantics through reconstruction. In this work, we ask the question: can we stand on the shoulders of well-established foundation models from well-represented modalities to bootstrap the pretraining of EEG foundation models? We first demonstrate that mainstream foundation models, such as those from vision and time series, transfer surprisingly well to EEG domain. To this end, we propose the Multi-Teacher Distillation Pretraining (MTDP) framework for pretraining EEG foundat
arXiv:2603.04478v1 Announce Type: new Abstract: Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely adapted from and inspired by the success of vision and language foundation models. However, unlike images and text, EEG datasets are notoriously expensive to collect and characterized by low signal-to-noise ratio. These challenges introduce difficulties in scaling the EEG foundation models and capturing the underlying neural semantics through reconstruction. In this work, we ask the question: can we stand on the shoulders of well-established foundation models from well-represented modalities to bootstrap the pretraining of EEG foundation models? We first demonstrate that mainstream foundation models, such as those from vision and time series, transfer surprisingly well to EEG domain. To this end, we propose the Multi-Teacher Distillation Pretraining (MTDP) framework for pretraining EEG foundation models via a two-stage multi-teacher distillation. In the first stage, we introduce a learnable gating network to fuse representations from diverse teachers (e.g., DINOv3 and Chronos) via a masked latent denoising objective. In the second stage, we distill the fused representation into an EEG foundation model. Extensive evaluations across 9 downstream tasks and 12 datasets demonstrate that our MTDP-based EEG foundation model outperforms its self-supervised counterparts while requiring only 25% of the pretraining data.
Executive Summary
This article proposes a novel pretraining framework, Multi-Teacher Distillation Pretraining (MTDP), to improve the performance of electroencephalogram (EEG) foundation models. By leveraging well-established foundation models from vision and time series, MTDP achieves state-of-the-art results on 9 downstream tasks and 12 datasets, outperforming self-supervised counterparts while requiring only 25% of the pretraining data. This breakthrough has significant implications for the development of EEG-based applications, particularly in the fields of neuroscience and healthcare. By alleviating the challenges associated with collecting and processing EEG data, MTDP paves the way for more efficient and accurate EEG-based systems.
Key Points
- ▸ Proposes a novel pretraining framework, MTDP, for EEG foundation models
- ▸ Leverages well-established foundation models from vision and time series
- ▸ Achieves state-of-the-art results on 9 downstream tasks and 12 datasets
Merits
Strength in leveraging transfer learning
The authors successfully apply transfer learning from well-established foundation models, demonstrating its effectiveness in the EEG domain.
Improved performance with reduced data requirements
MTDP achieves state-of-the-art results while requiring only 25% of the pretraining data, making it a more efficient and cost-effective approach.
Versatility and applicability
The framework is demonstrated to work effectively across 9 downstream tasks and 12 datasets, showcasing its versatility and potential applications in various EEG-based systems.
Demerits
Dependence on pre-existing models
The success of MTDP relies on the availability and quality of pre-existing foundation models from vision and time series, which may not always be the case.
Potential overfitting risks
The use of a learnable gating network in the first stage of MTDP may introduce overfitting risks, particularly if not properly regularized or fine-tuned.
Expert Commentary
This article marks a significant milestone in the development of EEG-based systems, leveraging the power of transfer learning to improve the performance of foundation models. While the proposed framework, MTDP, demonstrates impressive results, it is essential to consider the potential limitations and risks associated with its implementation. The authors' efforts to address these challenges and provide a comprehensive evaluation of the framework's performance are commendable. As the field continues to evolve, it will be crucial to explore the applicability of MTDP in various EEG-based systems and to investigate potential avenues for further improvement.
Recommendations
- ✓ Future research should focus on investigating the robustness of MTDP to different EEG datasets and tasks.
- ✓ The development of more efficient and scalable methods for training and fine-tuning the learnable gating network is essential to mitigate potential overfitting risks.