Large Transformers are Better EEG Learners
This work addresses the problem of limited EEG data for researchers and practitioners in medical and time series analysis by enabling transfer learning from pre-trained models, though it is incremental as it adapts existing methods to a new domain.
The paper tackled the challenge of applying large pre-trained transformer models to EEG data by proposing AdaCT, a plug-and-play adapter that converts time series data into formats suitable for vision or language transformers, achieving superior performance on benchmark datasets like Epileptic Seizure Recognition, Sleep-EDF, and UCI HAR.
Pre-trained large transformer models have achieved remarkable performance in the fields of natural language processing and computer vision. However, the limited availability of public electroencephalogram (EEG) data presents a unique challenge for extending the success of these models to EEG-based tasks. To address this gap, we propose AdaCT, plug-and-play Adapters designed for Converting Time series data into spatio-temporal 2D pseudo-images or text forms. Essentially, AdaCT-I transforms multi-channel or lengthy single-channel time series data into spatio-temporal 2D pseudo-images for fine-tuning pre-trained vision transformers, while AdaCT-T converts short single-channel data into text for fine-tuning pre-trained language transformers. The proposed approach allows for seamless integration of pre-trained vision models and language models in time series decoding tasks, particularly in EEG data analysis. Experimental results on diverse benchmark datasets, including Epileptic Seizure Recognition, Sleep-EDF, and UCI HAR, demonstrate the superiority of AdaCT over baseline methods. Overall, we provide a promising transfer learning framework for leveraging the capabilities of pre-trained vision and language models in EEG-based tasks, thereby advancing the field of time series decoding and enhancing interpretability in EEG data analysis. Our code will be available at https://github.com/wangbxj1234/AdaCE.