[1] V. Gilja et al., “Clinical translation of a high-performance neural prosthesis,” Nature medicine, vol. 21, no. 10, pp. 1142-1145, 2015.
[2] A. Kawala-Sterniuk et al., “Summary of over fifty years with brain-computer interfaces—a review,” Brain Sciences, vol. 11, no. 1, p. 43, 2021.
[3] J. L. Collinger et al., “High-performance neuroprosthetic control by an individual with tetraplegia,” The Lancet, vol. 381, no. 9866, pp. 557-564, 2013.
[4] L. R. Hochberg et al., “Neuronal ensemble control of prosthetic devices by a human with tetraplegia,” Nature, vol. 442, no. 7099, pp. 164-171, 2006.
[5] M. A. Lebedev and M. A. Nicolelis, “Brain-machine interfaces: From basic science to neuroprostheses and neurorehabilitation,” Physiological reviews, vol. 97, no. 2, pp. 767-837, 2017.
[6] E. C. Leuthardt, K. J. Miller, G. Schalk, R. P. Rao, and J. G. Ojemann, “Electrocorticography-based brain computer interface-the Seattle experience,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 14, no. 2, pp. 194-198, 2006.
[7] J. N. Mak and J. R. Wolpaw, “Clinical applications of brain-computer interfaces: current state and future prospects,” IEEE reviews in biomedical engineering, vol. 2, no. pp. 187-199, 2009.
[8] S. C. Colachis, C. F. Dunlap, N. V. Annetta, S. M. Tamrakar, M. A. Bockbrader, and D. A. Friedenberg, “Long-term intracortical microelectrode array performance in a human: A 5 year retrospective analysis,” Journal of Neural Engineering, vol. 18, no. 4, p. 0460d7, 2021.
[9] P. D. Ganzer et al., “Restoring the sense of touch using a sensorimotor demultiplexing neural interface,” Cell, vol. 181, no. 4, pp. 763-773. e12, 2020.
[10] S. J. Bensmaia and L. E. Miller, “Restoring sensorimotor function through intracortical interfaces: progress and looming challenges,” Nature Reviews Neuroscience, vol. 15, no. 5, pp. 313-325, 2014.
[11] J. E. O’Doherty et al., “Active tactile exploration using a brain–machine–brain interface,” Nature, vol. 479, no. 7372, pp. 228-231, 2011.
[12] K. E. Schroeder et al., “Robust tactile sensory responses in finger area of primate motor cortex relevant to prosthetic control,” Journal of neural engineering, vol. 14, no. 4, p. 046016, 2017.
[13] N. G. Hatsopoulos and A. J. Suminski, “Sensing with the motor cortex,” Neuron, vol. 72, no. 3, pp. 477-487, 2011.
[14] W. Jiang, F. Tremblay, and C. E. Chapman, “Context-dependent tactile texture-sensitivity in monkey M1 and S1 cortex,” Journal of Neurophysiology, vol. 120, no. 5, pp. 2334-2350, 2018.
[15] G. A. Tabot, S. S. Kim, J. E. Winberry, and S. J. Bensmaia, “Restoring tactile and proprioceptive sensation through a brain interface,” Neurobiology of disease, vol. 83, no. pp. 191-198, 2015.
[16] B. P. Delhaye, K. H. Long, and S. J. Bensmaia, “Neural basis of touch and proprioception in primate cortex,” Comprehensive Physiology, vol. 8, no. 4, p. 1575, 2018.
[17] O. J. Lutz and S. J. Bensmaia, “Proprioceptive representations of the hand in somatosensory cortex,” Current Opinion in Physiology, vol. 21, no. pp. 9-16, 2021.
[18] S. N. Flesher et al., “A brain-computer interface that evokes tactile sensations improves robotic arm control,” Science, vol. 372, no. 6544, pp. 831-836, 2021.
[19] J. E. O'Doherty, M. Lebedev, T. L. Hanson, N. Fitzsimmons, and M. A. Nicolelis, “A brain-machine interface instructed by direct intracortical microstimulation,” Frontiers in integrative neuroscience, vol. 3, no. p. 20, 2009.
[20] R. S. Johansson and J. R. Flanagan, “Coding and use of tactile signals from the fingertips in object manipulation tasks,” Nature Reviews Neuroscience, vol. 10, no. 5, pp. 345-359, 2009.
[21] Deo, Darrel R., et al. "Effects of peripheral haptic feedback on intracortical brain-computer interface control and associated sensory responses in motor cortex." IEEE transactions on haptics 14.4 (2021): 762-775.
[22] Vaswani, A. "Attention is all you need." Advances in Neural Information Processing Systems (2017).
[23] Heeger, David. "Poisson model of spike generation." Handout, University of Standford 5.1-13 (2000): 76.
[24] He, Kaiming, et al. "Masked autoencoders are scalable vision learners." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.
[25] Prusty, Sashikanta, Srikanta Patnaik, and Sujit Kumar Dash. "SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer." Frontiers in Nanotechnology 4 (2022): 972421.
[26] Liu, Xiuling, et al. "Parallel spatial–temporal self-attention CNN-based motor imagery classification for BCI." Frontiers in neuroscience 14 (2020): 587520.
[27] Hsiao, Ting-Yun, et al. "Filter-based deep-compression with global average pooling for convolutional networks." Journal of Systems Architecture 95 (2019): 9-18.
[28] Li, Zhi, et al. "Teeth category classification via seven‐layer deep convolutional neural network with max pooling and global average pooling." International Journal of Imaging Systems and Technology 29.4 (2019): 577-583.
[29] 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.
[30] F. R. Willett et al., “Hand knob area of premotor cortex represents the whole body in a compositional way,” Cell, vol. 181, no. 2, pp. 396-409. e26, 2020.
[31] N. K. Trzcinski, S. S. Hsiao, C. E. Connor, and M. Gomez-Ramirez, “Multi-finger Receptive Field Properties in Primary Somatosensory Cortex: A Revised Account of the Spatio-Temporal Integration Functions of Area 3b,” bioRxiv, vol. no. p. 2022.03. 21.485210, 2022.
[32] C. A. Chestek et al., “Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex,” Journal of neural engineering, vol. 8, no. 4, p. 045005, 2011.
[33] J. C. Barrese et al., “Failure mode analysis of silicon-based intracortical microelectrode arrays in non-human primates,” Journal of neural engineering, vol. 10, no. 6, p. 066014, 2013.
[34] Hughes, Christopher, et al. "Bidirectional brain-computer interfaces." Handbook of clinical neurology 168 (2020): 163-181.
[35] Ding, Xuehao, et al. "Information geometry of the retinal representation manifold." Advances in Neural Information Processing Systems 36 (2024).
[36] Keeley, Stephen L., et al. "Modeling statistical dependencies in multi-region spike train data." Current opinion in neurobiology 65 (2020): 194-202.
[37] Zhou, Ding, and Xue-Xin Wei. "Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE." Advances in Neural Information Processing Systems 33 (2020): 7234-7247.
[38] Maimon, Gaby, and John A. Assad. "Beyond Poisson: increased spike-time regularity across primate parietal cortex." Neuron 62.3 (2009): 426-440.
[39] Zambrano, Davide, et al. "Sparse computation in adaptive spiking neural networks." Frontiers in neuroscience 12 (2019): 987.