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 (HCT) block that
integrates a Fine-grained Temporal Learning (FTL) branch into Transformers,
effectively discerning coarse-to-fine temporal patterns; and (2) a Dense
Information Purification (DIP) module, which utilizes multi-level, purified
temporal information to enhance decoding accuracy. Comprehensive experiments on
three representative cognitive tasks consistently verify the generalizability
of our proposed EEG-Deformer, demonstrating that it either outperforms existing
state-of-the-art methods or is comparable to them. Visualization results show
that EEG-Deformer learns from neurophysiologically meaningful brain regions for
the corresponding cognitive tasks. The source code can be found at
this https URL

通过将 Hierarchical Coarse-to-Fine Transformer (HCT) 和 Dense Information Purification (DIP) 模块结合进 CNN-Transformer，我们提出了 EEG-Deformer 算法，有效地学习了脑电图（EEG）信号中的时间动态，进而解码脑活动，实验证明它在三个认知任务上要么优于现有的最先进方法，要么与其相媲美。