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Large Transformers are Better EEG Learners
April 16, 2024, 4:45 a.m. | Bingxin Wang, Xiaowen Fu, Yuan Lan, Luchan Zhang, Wei Zheng, Yang Xiang
cs.LG updates on arXiv.org arxiv.org
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 …
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