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In this paper, we propose a deep model based on graph convolutional networks for emotion recognition using EEG data. The model encodes spatial and temporal features of EEG channels and learns relationships between nodes through a self-attention mechanism, capturing spatio-temporal synchrony in brain regions. Experimental results show that our model outperforms existing approaches, with the attention mechanism contributing significantly to classification accuracy. In particular, the attention scores provide insights into how EEG channels influence each other at different times, revealing spatio-temporal patterns of brain connectivity related to emotions.