  Using deep learning methods to classify EEG signals can accurately identify
people's emotions. However, existing studies have rarely considered the
application of the information in another domain's representations to feature
selection in the time-frequency domain. We propose a classification network of
EEG signals based on the cross-domain feature fusion method, which makes the
network more focused on the features most related to brain activities and
thinking changes by using the multi-domain attention mechanism. In addition, we
propose a two-step fusion method and apply these methods to the EEG emotion
recognition network. Experimental results show that our proposed network, which
combines multiple representations in the time-frequency domain and spatial
domain, outperforms previous methods on public datasets and achieves
state-of-the-art at present.
