Abstract
In this paper, a novel multichannel EEG emotion recognition method based on sparse graphic attention long short-term memory (SGA-LSTM) is proposed. The basic idea of SGA-LSTM is to adopt graph structure modeling EEG signals to enhance the discriminative ability of EEG channels carrying more emotion information while alleviate the importance of the EEG channels carrying less emotion information. To this end, we employ two graphic branches. One branch generates global features reflecting the intrinsic relationship between EEG channels and the other generates an attention vector guiding the global features to focus on specific EEG channels. Researches on brain emotion show that different brain regions may be related to different brain functions and the contribution of each EEG channel to one specific brain function are possibly sparse such that \(\ell _1\)-norm penalty is applied. Extensive experiments are conducted on our dry electrodes EEG database and MPED database. The experimental results show that the proposed method is superior to the state-of-the-art methods.
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Acknowledgment
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1305200, in part by the National Natural Science Foundation of China under Grant 61921004, Grant 81971282, Grant 61572009, Grant 61902064, and Grant 61906094, and in part by the Fundamental Research Funds for the Central Universities under Grants 2242018K3DN01, Grant 2242019K40047, and Grant 30919011232.
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Liu, S., Zheng, W., Song, T., Zong, Y. (2019). Sparse Graphic Attention LSTM for EEG Emotion Recognition. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Communications in Computer and Information Science, vol 1142. Springer, Cham. https://doi.org/10.1007/978-3-030-36808-1_75
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DOI: https://doi.org/10.1007/978-3-030-36808-1_75
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