• Hochberg, L. R. et al. Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442, 164–171 (2006).


    Google Scholar
     

  • Gilja, V. et al. Clinical translation of a high-performance neural prosthesis. Nat. Med. 21, 1142–1145 (2015).


    Google Scholar
     

  • Pandarinath, C. et al. High performance communication by people with paralysis using an intracortical brain-computer interface. eLife 6, e18554 (2017).


    Google Scholar
     

  • Hochberg, L. R. et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012).


    Google Scholar
     

  • Collinger, J. L. et al. High-performance neuroprosthetic control by an individual with tetraplegia. Lancet 381, 557–564 (2013).


    Google Scholar
     

  • Wodlinger, B. et al. Ten-dimensional anthropomorphic arm control in a human brain-machine interface: difficulties, solutions, and limitations. J. Neural Eng. 12, 016011 (2015).


    Google Scholar
     

  • Aflalo, T. et al. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human. Science 348, 906–910 (2015).


    Google Scholar
     

  • Edelman, B. J. et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Sci. Robot. 4, eaaw6844 (2019).


    Google Scholar
     

  • Reddy, S., Dragan, A. D. & Levine, S. Shared autonomy via deep reinforcement learning. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2018.XIV.005 (RSS, 2018).

  • Laghi, M., Magnanini, M., Zanchettin, A. & Mastrogiovanni, F. Shared-autonomy control for intuitive bimanual tele-manipulation. In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) 1–9 (IEEE, 2018).

  • Tan, W. et al. On optimizing interventions in shared autonomy. In Proc. AAAI Conference on Artificial Intelligence 5341–5349 (AAAI, 2022).

  • Yoneda, T., Sun, L., Yang, G., Stadie, B. & Walter, M. To the noise and back: diffusion for shared autonomy. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2023.XIX.014 (RSS, 2023).

  • Peng, Z., Mo, W., Duan, C., Li, Q. & Zhou, B. Learning from active human involvement through proxy value propagation. Adv. Neural Inf. Process. Syst. 36, 20552–20563 (2023).


    Google Scholar
     

  • McMahan, B. J., Peng, Z., Zhou, B. & Kao, J. C. Shared autonomy with IDA: interventional diffusion assistance. Adv. Neural Inf. Process. Syst. 37, 27412–27425 (2024).

  • Shannon, C. E. Prediction and entropy of printed English. Bell Syst. Tech. J. 30, 50–64 (1951).

  • Karpathy, A., Johnson, J. & Fei-Fei, L. Visualizing and understanding recurrent networks. In International Conference on Learning Representations https://openreview.net/pdf/71BmK0m6qfAE8VvKUQWB.pdf (ICLR, 2016).

  • Radford, A. et al. Language models are unsupervised multitask learners. OpenAI https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf (2019).

  • Gilja, V. et al. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15, 1752–1757 (2012).


    Google Scholar
     

  • Dangi, S., Orsborn, A. L., Moorman, H. G. & Carmena, J. M. Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces. Neural Comput. 25, 1693–1731 (2013).

    MathSciNet 

    Google Scholar
     

  • Orsborn, A. L. et al. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82, 1380–1393 (2014).


    Google Scholar
     

  • Silversmith, D. B. et al. Plug-and-play control of a brain–computer interface through neural map stabilization. Nat. Biotechnol. 39, 326–335 (2021).


    Google Scholar
     

  • Kim, S.-P., Simeral, J. D., Hochberg, L. R., Donoghue, J. P. & Black, M. J. Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia. J. Neural Eng. 5, 455 (2008).


    Google Scholar
     

  • Sussillo, D. et al. A recurrent neural network for closed-loop intracortical brain–machine interface decoders. J. Neural Eng. 9, 026027 (2012).


    Google Scholar
     

  • Sussillo, D., Stavisky, S. D., Kao, J. C., Ryu, S. I. & Shenoy, K. V. Making brain–machine interfaces robust to future neural variability. Nat. Commun. 7, 13749 (2016).


    Google Scholar
     

  • Kao, J. C. et al. Single-trial dynamics of motor cortex and their applications to brain-machine interfaces. Nat. Commun. 6, 7759 (2015).


    Google Scholar
     

  • Kao, J. C., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Trans. Biomed. Eng. 64, 935–945 (2016).


    Google Scholar
     

  • Shenoy, K. V. & Carmena, J. M. Combining decoder design and neural adaptation in brain-machine interfaces. Neuron 84, 665–680 (2014).


    Google Scholar
     

  • Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. J. Neural Eng. 15, 056013 (2018).


    Google Scholar
     

  • Forenzo, D., Zhu, H., Shanahan, J., Lim, J. & He, B. Continuous tracking using deep learning-based decoding for noninvasive brain–computer interface. PNAS Nexus 3, pgae145 (2024).


    Google Scholar
     

  • Pfurtscheller, G. & Da Silva, F. L. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin. Neurophysiol. 110, 1842–1857 (1999).


    Google Scholar
     

  • Olsen, S. et al. An artificial intelligence that increases simulated brain–computer interface performance. J. Neural Eng. 18, 046053 (2021).


    Google Scholar
     

  • Schulman, J., Wolski, F., Dhariwal, P., Radford, A. & Klimov, O. Proximal policy optimization algorithms. Preprint at https://arxiv.org/abs/1707.06347 (2017).

  • Liu, S. et al. Grounding DINO: marrying DINO with grounded pre-training for open-set object detection. In 18th European Conference 38–55 (ACM, 2024).

  • Golub, M. D., Yu, B. M., Schwartz, A. B. & Chase, S. M. Motor cortical control of movement speed with implications for brain-machine interface control. J. Neurophysiol. 112, 411–429 (2014).


    Google Scholar
     

  • Sachs, N. A., Ruiz-Torres, R., Perreault, E. J. & Miller, L. E. Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface. J. Neural Eng. 13, 016009 (2016).


    Google Scholar
     

  • Kao, J. C., Nuyujukian, P., Ryu, S. I. & Shenoy, K. V. A high-performance neural prosthesis incorporating discrete state selection with hidden Markov models. IEEE Trans. Biomed. Eng. 64, 935–945 (2017).


    Google Scholar
     

  • Stieger, J. R. et al. Mindfulness improves brain–computer interface performance by increasing control over neural activity in the alpha band. Cereb. Cortex 31, 426–438 (2021).


    Google Scholar
     

  • Stieger, J. R., Engel, S. A. & He, B. Continuous sensorimotor rhythm based brain computer interface learning in a large population. Sci. Data 8, 98 (2021).


    Google Scholar
     

  • Edelman, B. J., Baxter, B. & He, B. EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. IEEE Trans. Biomed. Eng. 63, 4–14 (2016).


    Google Scholar
     

  • Scherer, R. et al. Individually adapted imagery improves brain-computer interface performance in end-users with disability. PLoS ONE 10, e0123727 (2015).


    Google Scholar
     

  • Millan, J. d. R. et al. A local neural classifier for the recognition of EEG patterns associated to mental tasks. IEEE Trans. Neural Netw. 13, 678–686 (2002).


    Google Scholar
     

  • Huang, D. et al. Decoding subject-driven cognitive states from EEG signals for cognitive brain–computer interface. Brain Sci. 14, 498 (2024).


    Google Scholar
     

  • Meng, J. et al. Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks. Sci. Rep. 6, 38565 (2016).


    Google Scholar
     

  • Jeong, J.-H., Shim, K.-H., Kim, D.-J. & Lee, S.-W. Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 28, 1226–1238 (2020).


    Google Scholar
     

  • Zhang, R. et al. NOIR: neural signal operated intelligent robots for everyday activities. In Proc. 7th Conference on Robot Learning 1737–1760 (PMLR, 2023).

  • Jeon, H. J., Losey, D. P. & Sadigh, D. Shared autonomy with learned latent actions. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2020.XVI.011 (RSS, 2020).

  • Javdani, S., Bagnell, J. A. & Srinivasa, S. S. Shared autonomy via hindsight optimization. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2015.XI.032 (RSS, 2015).

  • Newman, B. A. et al. HARMONIC: a multimodal dataset of assistive human-robot collaboration. Int. J. Robot. Res. 41, 3–11 (2022).

  • Jain, S. & Argall, B. Probabilistic human intent recognition for shared autonomy in assistive robotics. ACM Trans. Hum. Robot Interact. 9, 2 (2019).


    Google Scholar
     

  • Losey, D. P., Srinivasan, K., Mandlekar, A., Garg, A. & Sadigh, D. Controlling assistive robots with learned latent actions. In 2020 IEEE International Conference on Robotics and Automation (ICRA) 378–384 (IEEE, 2020).

  • Cui, Y. et al. No, to the right: online language corrections for robotic manipulation via shared autonomy. In Proc. 2023 ACM/IEEE International Conference on Human-Robot Interaction 93–101 (ACM, 2023).

  • Karamcheti, S. et al. Learning visually guided latent actions for assistive teleoperation. In Proc. 3rd Conference on Learning for Dynamics and Control 1230–1241 (PMLR, 2021).

  • Chi, C. et al. Diffusion policy: visuomotor policy learning via action diffusion. Int. J. Rob. Res. https://doi.org/10.1177/02783649241273668 (2024).

  • Brohan, A. et al. RT-1: robotics transformer for real-world control at scale. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2023.XIX.025 (RSS, 2023).

  • Brohan, A. et al. RT-2: vision-language-action models transfer web knowledge to robotic control. In Proc. 7th Conference on Robot Learning 2165–2183 (PMLR, 2023).

  • Nair, S., Rajeswaran, A., Kumar, V., Finn, C. & Gupta, A. R3M: a universal visual representation for robot manipulation. In Proc. 6th Conference on Robot Learning 892–909 (PMLR, 2023).

  • Ma, Y. J. et al. VIP: towards universal visual reward and representation via value-implicit pre-training. In 11th International Conference on Learning Representations https://openreview.net/pdf?id=YJ7o2wetJ2 (ICLR, 2023).

  • Khazatsky, A. et al. DROID: a large-scale in-the-wild robot manipulation dataset. In Proc. Robotics: Science and Systems https://doi.org/10.15607/RSS.2024.XX.120 (RSS, 2024).

  • Open X-Embodiment Collaboration. Open X-Embodiment: robotic learning datasets and RT-X models. In 2024 IEEE International Conference on Robotics and Automation (ICRA) 6892–6903 (IEEE, 2024).

  • Willett, F. R. et al. A high-performance speech neuroprosthesis. Nature 620, 1031–1036 (2023).


    Google Scholar
     

  • Leonard, M. K. et al. Large-scale single-neuron speech sound encoding across the depth of human cortex. Nature 626, 593–602 (2024).


    Google Scholar
     

  • Card, N. S. et al. An accurate and rapidly calibrating speech neuroprosthesis. N. Engl. J. Med. 391, 609–618 (2024).


    Google Scholar
     

  • Sato, M. et al. Scaling law in neural data: non-invasive speech decoding with 175 hours of EEG data. Preprint at https://arxiv.org/abs/2407.07595 (2024).

  • Kaifosh, P., Reardon, T. R. & CTRL-labs at Reality Labs. A generic non-invasive neuromotor interface for human–computer interaction. Nature https://doi.org/10.1038/s41586-025-09255-w (2025).

  • Zeng, H. et al. Semi-autonomous robotic arm reaching with hybrid gaze-brain machine interface. Front. Neurorobot. 13, 111 (2019).


    Google Scholar
     

  • Shafti, A., Orlov, P. & Faisal, A. A. Gaze-based, context-aware robotic system for assisted reaching and grasping. In 2019 International Conference on Robotics and Automation 863–869 (IEEE, 2019).

  • Argall, B. D. Autonomy in rehabilitation robotics: an intersection. Annu. Rev. Control Robot. Auton. Syst. 1, 441–463 (2018).


    Google Scholar
     

  • Nuyujukian, P. et al. Monkey models for brain-machine interfaces: the need for maintaining diversity. In Proc. 33rd Annual Conference of the IEEE EMBS 1301–1305 (IEEE, 2011).

  • Suminski, A. J., Tkach, D. C., Fagg, A. H. & Hatsopoulos, N. G. Incorporating feedback from multiple sensory modalities enhances brain-machine interface control. J. Neurosci. 30, 16777–16787 (2010).


    Google Scholar
     

  • Kaufman, M. T. et al. The largest response component in motor cortex reflects movement timing but not movement type. eNeuro 3, ENEURO.0085–16.2016 (2016).


    Google Scholar
     

  • Dangi, S. et al. Continuous closed-loop decoder adaptation with a recursive maximum likelihood algorithm allows for rapid performance acquisition in brain-machine interfaces. Neural Comput. 26, 1811–1839 (2014).


    Google Scholar
     

  • Fitts, P. M. The information capacity of the human motor system in controlling the amplitude of movement. J. Exp. Psychol. 47, 381 (1954).


    Google Scholar
     

  • Gramfort, A. et al. MNE software for processing MEG and EEG data. NeuroImage 86, 446–460 (2014).


    Google Scholar
     

  • Lee, J. Y. et al. Data: brain–computer interface control with artificial intelligence copilots. Zenodo https://doi.org/10.5281/zenodo.15165133 (2025).

  • Lee, J. Y. et al. kaolab-research/bci_raspy. Zenodo https://doi.org/10.5281/zenodo.15164641 (2025).

  • Lee, J. Y. et al. kaolab-research/bci_plot. Zenodo https://doi.org/10.5281/zenodo.15164643 (2025).