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EEG-based emotion recognition via capsule network with channel-wise attention and LSTM models


Emotion is a kind of psychological and physical state that people produce to objective things. Accurate recognition of emotions is very important in the field of human-machine interface. It is still challenging to extract discriminative features used for electroencephalogram (EEG) emotion recognition which contain the subtle spatial feature of EEG signal and the temporal representations of EEG signal. In order to overcome these challenges, the paper proposes a model based on the capsule network for multi-channel EEG emotion recognition, which combines the attention mechanism and the LSTM network. First, the channel-wise attention mechanism is used to adaptively assign different weights to each channel, then the CapsNet is used to extract the spatial features of the EEG channel, and LSTM is used to extract temporal features of the EEG sequences. The paper proposed method achieves average accuracy of 97.17%, 97.34% and 96.50% on the valence, arouse and dominance of the DEAP dataset, respectively. These results show that the proposed method is better than the current state-of-the-art method.

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The financial support for this work provided by the Cooperative Education Fund of China Ministry of Education (201702113002, 201801193119) and the Scientific Research Fund of Hunan Provincial Education Department (20A191) are greatly appreciated by the authors.

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Correspondence to Xiaoliang Wang.

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Deng, L., Wang, X., Jiang, F. et al. EEG-based emotion recognition via capsule network with channel-wise attention and LSTM models. CCF Trans. Pervasive Comp. Interact. (2021).

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  • EEG
  • Emotion recognition
  • Capsule network
  • LSTM
  • Channel-wise attention