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Recognizing the feelings of human beings plays a critical role in our dailycommunication. Neuroscience has demonstrated that different emotion statespresent different degrees of activation in different brain regions, EEGfrequency bands and temporal stamps. In this paper, we propose a novelstructure to explore the informative EEG features for emotion recognition. Theproposed module, denoted by PST-Attention, consists of Positional, Spectral andTemporal Attention modules to explore more discriminative EEG features.Specifically, the Positional Attention module is to capture the activateregions stimulated by different emotions in the spatial dimension. The Spectraland Temporal Attention modules assign the weights of different frequency bandsand temporal slices respectively. Our method is adaptive as well as efficientwhich can be fit into 3D Convolutional Neural Networks (3D-CNN) as a plug-inmodule. We conduct experiments on two real-world datasets. 3D-CNN combined withour module achieves promising results and demonstrate that the PST-Attention isable to capture stable patterns for emotion recognition from EEG.

Positional-Spectral-Temporal Attention in 3D Convolutional Neural Networks for EEG Emotion Recognition