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Using deep learning methods to classify EEG signals can accurately identifypeople's emotions. However, existing studies have rarely considered theapplication of the information in another domain's representations to featureselection in the time-frequency domain. We propose a classification network ofEEG signals based on the cross-domain feature fusion method, which makes thenetwork more focused on the features most related to brain activities andthinking changes by using the multi-domain attention mechanism. In addition, wepropose a two-step fusion method and apply these methods to the EEG emotionrecognition network. Experimental results show that our proposed network, whichcombines multiple representations in the time-frequency domain and spatialdomain, outperforms previous methods on public datasets and achievesstate-of-the-art at present.

Improving EEG-based Emotion Recognition by Fusing Time-frequency And Spatial Representations