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EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces
May 3, 2024, 4:53 a.m. | Yi Ding, Yong Li, Hao Sun, Rui Liu, Chengxuan Tong, Cuntai Guan
cs.LG updates on arXiv.org arxiv.org
Abstract: Effectively learning the temporal dynamics in electroencephalogram (EEG) signals is challenging yet essential for decoding brain activities using brain-computer interfaces (BCIs). Although Transformers are popular for their long-term sequential learning ability in the BCI field, most methods combining Transformers with convolutional neural networks (CNNs) fail to capture the coarse-to-fine temporal dynamics of EEG signals. To overcome this limitation, we introduce EEG-Deformer, which incorporates two main novel components into a CNN-Transformer: (1) a Hierarchical Coarse-to-Fine Transformer …
arxiv brain computer convolutional cs.lg eeg eess.sp interfaces q-bio.nc transformer type
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