[4] J. Jude, M. G. Perich, L. E. Miller, and M. H. Hennig, "Robust alignment of cross-session recordings of neural population activity by behaviour via unsupervised domain adaptation," arXiv preprint arXiv:2202.06159, 2022.
[5] Z. Wan, R. Yang, M. Huang, N. Zeng, and X. Liu, “A review on transfer learning in eeg signal analysis,” Neurocomputing, vol. 421, pp. 1-14, 2021.
[6] S. Niu, Y. Liu, J. Wang, and H. Song, “A decade survey of transfer learning (2010–2020),” IEEE Transactions on Artificial Intelligence, vol. 1, no. 2, pp. 151–166, 2020.
[7] L. Hu, A. Mouraux, Y. Hu, and G. D. Iannetti, “A novel approach for enhancing the signal-to-noise ratio and detecting automatically event-related potentials (erps) in single trials,” Neuroimage, vol. 50, no. 1, pp. 99–111, 2010.
[8] J. A. Gallego, M. G. Perich, R. H. Chowdhury, S. A. Solla, and L. E. Miller, "Long-term stability of cortical population dynamics underlying consistent behavior," Nature neuroscience, vol. 23, no. 2, pp. 260-270, 2020.
[9] J. A. Gallego, M. G. Perich, R. H. Chowdhury, S. A. Solla, and L. E. Miller, "A stable, long-term cortical signature underlying consistent behavior," BioRxiv, p. 447441, 2018.
[10] M. M. Churchland, J. P. Cunningham, M. T. Kaufman, J. D. Foster, P. Nuyujukian, S. I. Ryu, and K. V. Shenoy, “Neural population dynamics during reaching,” Nature, vol. 487, no. 7405, pp. 51–56, 2012.
[11] H. Sohn, D. Narain, N. Meirhaeghe, and M. Jazayeri, “Bayesian computation through cortical latent dynamics,” Neuron, vol. 103, no. 5, pp. 934–947, 2019.
[12] S. Saxena, A. A. Russo, J. Cunningham, and M. M. Churchland, “Motor cortex activity across movement speeds is predicted by network-level strategies for generating muscle activity,” *Elife*, vol. 11, p. e67620, 2022.
[13] R. Liu, M. Azabou, M. Dabagia, C.-H. Lin, M. Gheshlaghi Azar, K. Hengen, M. Valko, and E. Dyer, "Drop, swap, and generate: A self-supervised approach for generating neural activity," Advances in neural information processing systems, vol. 34, pp. 10587-10599, 2021.
[14] J. A. Gallego, M. G. Perich, S. N. Naufel, C. Ethier, S. A. Solla, and L. E. Miller, "Cortical population activity within a preserved neural manifold underlies multiple motor behaviors," Nature communications, vol. 9, no. 1, p. 4233, 2018.
[15] R. Mitchell-Heggs, S. Prado, G. P. Gava, M. A. Go, and S. R. Schultz, “Neural manifold analysis of brain circuit dynamics in health and disease,” Journal of Computational Neuroscience, vol. 51, no. 1, pp. 1–21, 2023.
[16] B. M. Karpowicz, Y. H. Ali, L. N. Wimalasena, A. R. Sedler, M. R. Keshtkaran, K. Bodkin, X. Ma, L. E. Miller, and C. Pandarinath, "Stabilizing brain-computer interfaces through alignment of latent dynamics," bioRxiv, 2022.
[17] A. D. Degenhart, W. E. Bishop, E. R. Oby, E. C. Tyler-Kabara, S. M. Chase, A. P. Batista, and B. M. Yu, "Stabilization of a brain-computer interface via the alignment of low-dimensional spaces of neural activity," Nature biomedical engineering, vol. 4, no. 7, pp. 672-685, 2020.
[18] J. Lee, M. Dabagia, E. Dyer, and C. Rozell, "Hierarchical optimal transport for multimodal distribution alignment," Advances in Neural Information Processing Systems, vol. 32, 2019.
[19] R. Liu, M. Azabou, M. Dabagia, J. Xiao, and E. Dyer, "Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers," Advances in neural information processing systems, vol. 35, pp. 2377-2391, 2022.
[20] A. Farshchian, J. A. Gallego, J. P. Cohen, Y. Bengio, L. E. Miller, and S. A. Solla, "Adversarial domain adaptation for stable brain-machine interfaces," arXiv preprint arXiv:1810.00045, 2018.
[21] X. Ma, F. Rizzoglio, E. J. Perreault, L. E. Miller, and A. Kennedy, "Using adversarial networks to extend brain computer interface decoding accuracy over time," bioRxiv, pp. 2022-08, 2022.
[22] W. Zhao, L. Mou, J. Chen, Y. Bo, and W. J. Emery, “Incorporating metric learning and adversarial network for seasonal invariant change detection,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 4, pp. 2720-2731, 2019.
[23] Z. Zhang, M. Li, and J. Yu, “On the convergence and mode collapse of gan,” in SIGGRAPH Asia 2018 Technical Briefs, pp. 1–4, 2018.