EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces

arXiv:2405.00719v1 Announce Type: cross
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