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In previous studies, decoding electroencephalography (EEG) signals has not considered the topological relationship of EEG electrodes.
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., et al.: Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101
2000
Earlier work this paper cites.
Dhillon, I.S., Guan, Y., Kulis, B.: Weighted graph cuts without eigenvectors a multilevel approach. IEEE transactions on pattern analysis and machine intelligence 29
2007
Earlier work this paper cites.
Shuman, D.I., Narang, S.K., Frossard, P., Ortega, A., Vandergheynst, P.: The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE signal processing magazine 30
2013
Earlier work this paper cites.
Lv, J., Jiang, X., Li, X., Zhu, D., Zhang, S., Zhao, S., Chen, H., Zhang, T., Hu, X., Han, J., et al.: Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function. IEEE Transactions on Biomedical Engineering 62
2014
Earlier work this paper cites.
Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Advances in neural information processing systems. pp. 3844–3852 (2016)
2016
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies. pp. 1480–1489 (2016)
2016
Earlier work this paper cites.
Lv, J., Nguyen, V.T., van der Meer, J., Breakspear, M., Guo, C.C.: N-way decomposition: Towards linking concurrent eeg and fmri analysis during natural stimulus. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 382–389. Springer (2017)
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Cited alongside, same era.
Vidaurre, D., Smith, S.M., Woolrich, M.W.: Brain network dynamics are hierarchically organized in time. Proceedings of the National Academy of Sciences 114
2017
Cited alongside, same era.
Biasiucci, A., Leeb, R., Iturrate, I., Perdikis, S., Al-Khodairy, A., Corbet, T., Schnider, A., Schmidlin, T., Zhang, H., Bassolino, M., et al.: Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nature communications 9
Wang, X.h., Zhang, T., Xu, X.m., Chen, L., Xing, X.f., Chen, C.P.: Eeg emotion recognition using dynamical graph convolutional neural networks and broad learning system. In: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). pp. 1240–1244. IEEE (2018)
2018
Later among the works it cites.
Li, G., Muller, M., Thabet, A., Ghanem, B.: Deepgcns: Can gcns go as deep as cnns? In: Proceedings of the IEEE International Conference on Computer Vision. pp. 9267–9276 (2019)
2019
Later among the works it cites.
Mahmood, M., Mzurikwao, D., Kim, Y.S., Lee, Y., Mishra, S., Herbert, R., Duarte, A., Ang, C.S., Yeo, W.H.: Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm. Nature Machine Intelligence 1
2019
Later among the works it cites.
Shanechi, M.M.: Brain–machine interfaces from motor to mood. Nature neuroscience 22
2019
Later among the works it cites.
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2018
Cited alongside, same era.
Dose, H., Møller, J.S., Iversen, H.K., Puthusserypady, S.: An end-to-end deep learning approach to mi-eeg signal classification for bcis. Expert Systems with Applications 114
2018
Cited alongside, same era.
Ma, X., Qiu, S., Du, C., Xing, J., He, H.: Improving eeg-based motor imagery classification via spatial and temporal recurrent neural networks. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). pp. 1903–1906. IEEE (2018)
2018
Cited alongside, same era.
Song, T., Zheng, W., Song, P., Cui, Z.: Eeg emotion recognition using dynamical graph convolutional neural networks. IEEE Transactions on Affective Computing (2018)
2018
Cited alongside, same era.
Zhang, D., Yao, L., Chen, K., Wang, S., Haghighi, P.D., Sullivan, C.: A graph-based hierarchical attention model for movement intention detection from eeg signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27
2019
Later among the works it cites.
Zhang, T., Wang, X., Xu, X., Chen, C.P.: Gcb-net: Graph convolutional broad network and its application in emotion recognition. IEEE Transactions on Affective Computing (2019)
2019
Later among the works it cites.
Hou, Y., Zhou, L., Jia, S., Lun, X.: A novel approach of decoding eeg four-class motor imagery tasks via scout esi and cnn. Journal of neural engineering 17
2020
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