Large Transformers are Better EEG Learners

• URL: http://arxiv.org/abs/2308.11654v2
• Date: Sat, 13 Apr 2024 05:11:03 GMT
• Title: Large Transformers are Better EEG Learners
• Authors: Bingxin Wang, Xiaowen Fu, Yuan Lan, Luchan Zhang, Wei Zheng, Yang Xiang,
• Abstract summary: AdaCT, plug-and-play Adapters designed for Time series data into 2D pseudo-images or text forms. AdaCTI transforms multi-channel or lengthy single-channel time series data into pseudo-images for fine-tuning pre-trained vision transformers. AdaCT-T converts short single-channel data into text for fine-tuning pre-trained language transformers.
• Score: 8.930281191465088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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