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- W3200940908 abstract "Automatic emotion recognition based on electroencephalogram (EEG) is a challenging task in Brain Machine Interfaces (BMI). Since it is still not very clear about the intrinsic connection relationship among the various EEG channels, it is still a challenging task of how to better represent the topology of EEG channels for emotion recognition. On the other hand, the intensity of the emotion may vary along the different time instants, which would affect the recognition accuracy of emotion. To tackle the above issues, in this paper, we propose a novel multichannel EEG emotion recognition method called attention-based spatiotemporal graphic long short-term memory (ASTG-LSTM), in which a Dynamic Structured Learning (DSL) branch that focuses on the most emotion-relevant connectivity of the brain is incorporated to represent inter-channel connections of the EEG signals. In addition, a specific spatio-temporal attention is embedded into the DSL branch to improve the invariance ability against the emotional intensity fluctuation. Extensive experiments on: DEAP and DREAMER are conducted and the experimental results indicate that the proposed ASTG-LSTM model improves the EEG emotion recognition performance compared with many state-of-the-art approaches." @default.
- W3200940908 created "2021-09-27" @default.
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- W3200940908 date "2021-07-18" @default.
- W3200940908 modified "2023-10-13" @default.
- W3200940908 title "Attention-based Spatio-Temporal Graphic LSTM for EEG Emotion Recognition" @default.
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- W3200940908 doi "https://doi.org/10.1109/ijcnn52387.2021.9534443" @default.
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