  At present, people usually use some methods based on convolutional neural
networks (CNNs) for Electroencephalograph (EEG) decoding. However, CNNs have
limitations in perceiving global dependencies, which is not adequate for common
EEG paradigms with a strong overall relationship. Regarding this issue, we
propose a novel EEG decoding method that mainly relies on the attention
mechanism. The EEG data is firstly preprocessed and spatially filtered. And
then, we apply attention transforming on the feature-channel dimension so that
the model can enhance more relevant spatial features. The most crucial step is
to slice the data in the time dimension for attention transforming, and finally
obtain a highly distinguishable representation. At this time, global averaging
pooling and a simple fully-connected layer are used to classify different
categories of EEG data. Experiments on two public datasets indicate that the
strategy of attention transforming effectively utilizes spatial and temporal
features. And we have reached the level of the state-of-the-art in
multi-classification of EEG, with fewer parameters. As far as we know, it is
the first time that a detailed and complete method based on the transformer
idea has been proposed in this field. It has good potential to promote the
practicality of brain-computer interface (BCI). The source code can be found
at: \textit{https://github.com/anranknight/EEG-Transformer}.
