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We present a novel deep neural architecture for learning electroencephalogram (EEG).
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2015
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2016
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2016
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2016
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2017
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W.-L. Zheng, J.-Y. Zhu, and B.-L. Lu, “Identifying stable patterns over time for emotion recognition from eeg,” IEEE Transactions on Affective Computing , 2017
2017
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Later among the works it cites.
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2019
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2019
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Y. Li, W. Zheng, L. Wang, Y. Zong, and Z. Cui, “From regional to global brain: A novel hierarchical spatial-temporal neural network model for eeg emotion recognition,” IEEE Transactions on Affective Computing , 2019
2019
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2019
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2019
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2019
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2020
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2020
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2021
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2021
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J. Fumanal-Idocin, Y.-K. Wang, C.-T. Lin, J. Fernández, J. A. Sanz, and H. Bustince, “Motor-imagery-based brain–computer interface using signal derivation and aggregation functions,” IEEE Transactions on Cybernetics , vol. 52, no. 8, pp. 7944–7955, 2021
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Y. Li, J. Chen, F. Li, B. Fu, H. Wu, Y. Ji, Y. Zhou, Y. Niu, G. Shi, and W. Zheng, “Gmss: Graph-based multi-task self-supervised learning for eeg emotion recognition,” IEEE Transactions on Affective Computing , 2022
2022
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X. Cheng, W. Wei, C. Du, S. Qiu, S. Tian, X. Ma, and H. He, “Vigilancenet: Decouple intra-and inter-modality learning for multimodal vigilance estimation in rsvp-based bci,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 209–217
2022
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H. Altaheri, G. Muhammad, and M. Alsulaiman, “Physics-informed attention temporal convolutional network for eeg-based motor imagery classification,” IEEE Transactions on Industrial Informatics , vol. 19, no. 2, pp. 2249–2258, 2022
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2023
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D.-H. Kim, D.-H. Shin, and T.-E. Kam, “Bridging the bci illiteracy gap: a subject-to-subject semantic style transfer for eeg-based motor imagery classification,” Frontiers in Human Neuroscience , vol. 17, p. 1194751, 2023
2023
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