ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data
This incremental advance benefits neuroscience and fields with limited data by enabling reuse of pretrained models on unrelated tasks.
The study tackled EEG regression by fine-tuning a Vision Transformer pretrained on ImageNet, achieving a notable performance increase compared to models without such pretraining, challenging traditional views on model generalization.
In this study, we demonstrate the application of a hybrid Vision Transformer (ViT) model, pretrained on ImageNet, on an electroencephalogram (EEG) regression task. Despite being originally trained for image classification tasks, when fine-tuned on EEG data, this model shows a notable increase in performance compared to other models, including an identical architecture ViT trained without the ImageNet weights. This discovery challenges the traditional understanding of model generalization, suggesting that Transformer models pretrained on seemingly unrelated image data can provide valuable priors for EEG regression tasks with an appropriate fine-tuning pipeline. The success of this approach suggests that the features extracted by ViT models in the context of visual tasks can be readily transformed for the purpose of EEG predictive modeling. We recommend utilizing this methodology not only in neuroscience and related fields, but generally for any task where data collection is limited by practical, financial, or ethical constraints. Our results illuminate the potential of pretrained models on tasks that are clearly distinct from their original purpose.