  Both the temporal dynamics and spatial correlations of Electroencephalogram
(EEG), which contain discriminative emotion information, are essential for the
emotion recognition. However, some redundant information within the EEG signals
would degrade the performance. Specifically,the subjects reach prospective
intense emotions for only a fraction of the stimulus duration. Besides, it is a
challenge to extract discriminative features from the complex spatial
correlations among a number of electrodes. To deal with the problems, we
propose a transformer-based model to robustly capture temporal dynamics and
spatial correlations of EEG. Especially, temporal feature extractors which
share the weight among all the EEG channels are designed to adaptively extract
dynamic context information from raw signals. Furthermore, multi-head
self-attention mechanism within the transformers could adaptively localize the
vital EEG fragments and emphasize the essential brain regions which contribute
to the performance. To verify the effectiveness of the proposed method, we
conduct the experiments on two public datasets, DEAP and MAHNOBHCI. The results
demonstrate that the proposed method achieves outstanding performance on
arousal and valence classification.
