[17] E. Gibson, N. J. Lobaugh, S. Joordens, and A. R. McIntosh. EEG variability: Task-driven or subject-driven signal of interest? NeuroImage, 252:119034, 2022.
[18] A. L. Goldberger, L. A. Amaral, L. Glass, J. M. Hausdorff, P. C. Ivanov, R. G. Mark, J. E. Mietus, G. B. Moody, C.-K. Peng, and H. E. Stanley. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23): e215–e220, 2000.
[19] M. Golmohammadi, A. H. Harati Nejad Torbati, S. Lopez de Diego, I. Obeid, and J. Picone. Automatic analysis of EEGs using big data and hybrid deep learning architectures. Frontiers in Human Neuroscience, 13:76, 2019.
[20] M. Grosse-Wentrup and M. Buss. Multiclass common spatial patterns and information theoretic feature extraction. IEEE Transactions on Biomedical Engineering, 55(8):1991-2000, 2008.
[21] R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 1735-1742. IEEE, 2006.
[22] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016.
[23] G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-507, 2006.
[24] Z. Hu, Z. Zhang, Z. Liang, L. Zhang, L. Li, and G. Huang. A new perspective on individual reliability beyond group effect for event-related potentials: A multisensory investigation and computational modeling. NeuroImage, 250:118937, 2022.
[25] A. Jaiswal, A. R. Babu, M. Z. Zadeh, D. Banerjee, and F. Makedon. A survey on contrastive self-supervised learning. Technologies, 9(1):2, 2020.
[26] S. Jirayucharoensak, S. Pan-Ngum, and P. Israsena. EEG-based emotion recognition using deep learning network with principal component based covariate shift adaptation. The Scientific World Journal, 2014, 2014.
[27] H. Kameoka, T. Kaneko, K. Tanaka, and N. Hojo. StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks. In 2018 IEEE Spoken Language Technology Workshop (SLT), pages 266–273. IEEE, 2018.
[28] E. S. Kappenman, J. L. Farrens, W. Zhang, A. X. Stewart, and S. J. Luck. ERP CORE: An open resource for human event-related potential research. NeuroImage, 225:117465, 2021.
[29] B. Kemp, A. H. Zwinderman, B. Tuk, H. A. Kamphuisen, and J. J. Oberye. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Transactions on Biomedical Engineering, 47(9):1185-1194, 2000.
[30] D. P. Kingma and M. Welling. Auto-Encoding Variational Bayes. arXiv:1312.6114, 2013.
[31] D. Kobak and P. Berens. The art of using t-SNE for single-cell transcriptomics. Nature Communications, 10(1):5416, 2019.
[32] Z. J. Koles, M. S. Lazar, and S. Z. Zhou. Spatial patterns underlying population differences in the background EEG. *Brain Topography*, 2(4):275–284, 1990.
[33] A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60(6):84-90, 2017.
[34] M. Längkvist, L. Karlsson, and A. Loutfi. Sleep stage classification using unsupervised feature learning. Advances in Artificial Neural Systems, 2012.
[35] J. Li and A. Cichocki. Deep learning of multifractal attributes from motor imagery induced EEG. In International Conference on Neural Information Processing, pages 503-510. Springer, 2014.