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Pre-trained large transformer models have achieved remarkable performance inthe fields of natural language processing and computer vision. Since themagnitude of available labeled electroencephalogram (EEG) data is much lowerthan that of text and image data, it is difficult for transformer modelspre-trained from EEG to be developed as large as GPT-4 100T to fully unleashthe potential of this architecture. In this paper, we show that transformerspre-trained from images as well as text can be directly fine-tuned forEEG-based prediction tasks. We design AdaCE, plug-and-play Adapters forConverting EEG data into image as well as text forms, to fine-tune pre-trainedvision and language transformers. The proposed AdaCE module is highly effectivefor fine-tuning pre-trained transformers while achieving state-of-the-artperformance on diverse EEG-based prediction tasks. For example, AdaCE on thepre-trained Swin-Transformer achieves 99.6%, an absolute improvement of 9.2%,on the EEG-decoding task of human activity recognition (UCI HAR). Furthermore,we empirically show that applying the proposed AdaCE to fine-tune largerpre-trained models can achieve better performance on EEG-based predictingtasks, indicating the potential of our adapters for even larger transformers.The plug-and-play AdaCE module can be applied to fine-tuning most of thepopular pre-trained transformers on many other time-series data with multiplechannels, not limited to EEG data and the models we use. Our code will beavailable at https://github.com/wangbxj1234/AdaCE.

Large Transformers are Better EEG Learners