ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data
Ruiqi Yang Eric Modesitt
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.