Feature hypergraph representation learning on spatial-temporal correlations for EEG emotion recognition

Bagherzadeh S, Maghooli K, Shalbaf A, Maghsoudi A (2022) Emotion recognition using effective connectivity and pre-trained convolutional neural networks in EEG signals. Cogn Neurodyn 1–20

Bahari F, Janghorbani A (2013) EEG-based emotion recognition using recurrence plot analysis and k nearest neighbor classififier, Paper presented at 20th Iranian conference on biomedical engineering (ICBME). https://doi.org/10.1109/ICBME.2013.6782224

Bai S, Zhang F, Torr PH (2021) Hypergraph convolution and hypergraph attention. Pattern Recognit 110:107637

Article  Google Scholar 

Deng L, Wang X, Jiang F, Doss R (2021) EEG-based emotion recognition via capsule network with channel-wise attention and lstm models. CCF Trans Pervasive Comput Interact 3(4):425–435

Article  Google Scholar 

Deng X, Zhu J, Yang S (2021) Sfe-net: EEG-based emotion recognition with symmetrical spatial feature extraction. In: Proceedings of the 29th ACM international conference on multimedia, pp. 2391–2400

Ding Y, Robinson N, Zhang S, Zeng Q, Guan C (2021) Tsception: capturing temporal dynamics and spatial asymmetry from EEG for emotion recognition. arXiv preprint arXiv:2104.02935

Feng Y, You, H, Zhang, Z (2019) Hypergraph neural networks. Biomedical engineering, Paper presented at the Proceedings of the AAAI conference on artificial intelligence, 7(3), 162–175

Huang J, Yang J (2021) Unignn: a unified framework for graph and hypergraph neural networks. arXiv preprint arXiv:2105.00956

Jia Z, Lin Y, Cai X, Chen H, Gou H, Wang J (2020) Sst-emotionnet: spatial-spectral-temporal based attention 3d dense network for EEG emotion recognition. In: Proceedings of the 28th ACM international conference on multimedia, pp. 2909–2917

Jiang J, Wei Y, Feng Y, Cao J, Gao Y (2019) Dynamic hypergraph neural networks. In: IJCAI, pp. 2635–2641

Li Y, Wang L, Zheng W, Zong Y, Qi L, Cui Z, Zhang T, Song T (2020) A novel bi-hemispheric discrepancy model for EEG emotion recognition. IEEE Trans Cogn Dev Syst 13(2):354–367

Article  Google Scholar 

Li Y, Zheng W, Cui Z, Zhang T, Zong Y (2018) A novel neural network model based on cerebral hemispheric asymmetry for EEG emotion recognition. In: IJCAI, pp. 1561–1567

Li Y, Zheng W, Wang L, Zong Y, Cui Z (2019) From regional to global brain: a novel hierarchical spatial-temporal neural network model for EEG emotion recognition. IEEE Trans Affect Comput

Lotfi E, Akbarzadeh-T M-R (2014) Practical emotional neural networks. Neural Netw 59:61–72

Article  Google Scholar 

Lugo-Martinez J, Radivojac P (2017) Classification in biological networks with hypergraphlet kernels. arXiv preprint arXiv:1703.04823

Sawhney R, Agarwal S, Wadhwa A, Derr T, Shah RR (2021) Stock selection via spatiotemporal hypergraph attention network: a learning to rank approach. In: Proceedding of AAAI, 497–504

Shen F, Dai G, Lin G, Zhang J, Kong W, Zeng H (2020) EEG-based emotion recognition using 4D convolutional recurrent neural network. Cogn Neurodyn 14(6):815–828

Article  Google Scholar 

Song T, Zheng W, Song P, Cui Z (2018) EEG emotion recognition using dynamical graph convolutional neural networks. IEEE Trans Affect Comput 11(3):532–541

Article  Google Scholar 

Tuncer T, Dogan S, Subasi A (2021) Ledpatnet19: automated emotion recognition model based on nonlinear led pattern feature extraction function using EEG signals. Cogn Neurodyn 1–12

Wang XW, Nie D, Lu BL (2011) Eeg-based emotion recognition using frequency domain features and support vector machines (2011). Paper presented at international conference on neural information processing

Wang Z, Wang Y, Hu C, Yin Z, Song Y (2022) Transformers for EEG-based emotion recognition: a hierarchical spatial information learning model. IEEE Sens J

Xiao G, Shi M, Ye M, Xu B, Chen Z, Ren Q (2022) 4D attention-based neural network for EEG emotion recognition. Cogn Neurodyn 1–14

Xia X, Yin H, Yu J, Wang Q, Cui L, Zhang X (2021) Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, pp. 4503–4511

Yadati N, Nimishakavi M, Yadav P (2019) Hypergcn: a new method for training graph convolutional networks on hypergraphs. Adv Neural Inf process syst 32

Zhang D, Yao L, Chen K, Wang S, Haghighi PD, Sullivan C (2019) A graph-based hierarchical attention model for movement intention detection from EEG signals. IEEE Trans Neural Syst Rehabil Eng 27(11):2247–2253. https://doi.org/10.1109/TNSRE.2019.2943362

Article  PubMed  Google Scholar 

Zheng W-L, Lu B-L (2015) Investigating critical frequency bands and channels for eeg-based emotion recognition with deep neural networks. IEEE Trans. Auton. Ment. Dev. 7(3):162–175

Article  Google Scholar 

Zheng W-L, Liu W, Lu Y, Lu B-L, Cichocki A (2018) Emotionmeter: a multimodal framework for recognizing human emotions. IEEE Trans Cybern 49(3):1110–1122

Article  Google Scholar 

Zhong P, Wang, D, Miao C (2020) EEG-based emotion recognition using regularized graph neural networks. IEEE Transact Affect Comput

Zhong P, Wang D, Miao C (2020) EEG-based emotion recognition using regularized graph neural networks. IEEE Trans Affect Comput

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