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.

本研究展示了在脑电图（EEG）回归任务上，以 ImageNet 为预训练数据的混合 Vision Transformer（ViT）模型的应用。通过与其他模型进行比较，包括没有 ImageNet 权重的相同结构 ViT 模型，在 EEG 数据上微调后，该模型表现出显著的性能提升。这一发现挑战了对模型泛化的传统理解，表明在合适的微调流程下，预先训练在看似无关的图像数据上的 Transformer 模型可以为 EEG 回归任务提供有价值的先验知识。这种方法的成功暗示了 ViT 模型在视觉任务环境中提取的特征可以轻松地转化为 EEG 预测建模的目的。我们建议不仅在神经科学和相关领域，而且在任何由实际、财务或伦理限制所限制的任务中采用这种方法。我们的结果揭示了预训练模型在与其原始目的明显不同的任务上的潜力。