Sources and further reading

Quantitative claims were checked against the abstracts of the primary publications in October 2026. Preprints are marked and have not completed peer review. Entries 1, 17, 30, 59, 60, 61 and 62 are cited from the published record without being re-fetched during that check.

1. Dhakal V, Feit AM, Kristensson PO, Oulasvirta A. Observations on typing from 136 million keystrokes. Proc. CHI (2018). doi:10.1145/3173574.3174220
2. Coupé C, Oh YM, Dediu D, Pellegrino F. Different languages, similar encoding efficiency. Science Advances 5, eaaw2594 (2019). doi:10.1126/sciadv.aaw2594
3. Zheng J, Meister M. The unbearable slowness of being: why do we live at 10 bits/s? Neuron 113 (2025; online 2024). doi:10.1016/j.neuron.2024.11.008
4. Wolpaw JR et al. Brain–computer interfaces for communication and control. Clinical Neurophysiology 113, 767–791 (2002). doi:10.1016/S1388-2457(02)00057-3
5. Vidal JJ. Toward direct brain–computer communication. Annual Review of Biophysics and Bioengineering 2, 157–180 (1973). doi:10.1146/annurev.bb.02.060173.001105
6. Willett FR et al. High-performance brain-to-text communication via handwriting. Nature 593, 249–254 (2021). nature.com/articles/s41586-021-03506-2
7. Metzger SL et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature 620, 1037–1046 (2023). nature.com/articles/s41586-023-06443-4
8. Willett FR et al. A high-performance speech neuroprosthesis. Nature 620, 1031–1036 (2023). nature.com/articles/s41586-023-06377-x
9. Card NS et al. An accurate and rapidly calibrating speech neuroprosthesis. New England Journal of Medicine 391, 609–618 (2024). doi:10.1056/NEJMoa2314132
10. Moses DA et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. New England Journal of Medicine 385, 217–227 (2021). doi:10.1056/NEJMoa2027540
11. Tang J, LeBel A, Jain S, Huth AG. Semantic reconstruction of continuous language from non-invasive brain recordings. Nature Neuroscience 26, 858–866 (2023). nature.com/articles/s41593-023-01304-9
12. Scotti PS et al. Reconstructing the mind’s eye: fMRI-to-image with contrastive learning and diffusion priors. NeurIPS (2023). arXiv:2305.18274
13. Scotti PS et al. MindEye2: shared-subject models enable fMRI-to-image with 1 hour of data. ICML, PMLR 235 (2024). proceedings.mlr.press/v235/scotti24a
14. Gong Z et al. NeuroClips: towards high-fidelity and smooth fMRI-to-video reconstruction. NeurIPS (2024, oral). arXiv:2410.19452
15. Lévy J et al. Brain-to-text decoding: a non-invasive approach via typing. Preprint (2025). arXiv:2502.17480
16. Zhang M et al. Accurate decoding of natural sentences from non-invasive brain recordings. Preprint (June 2026). ai.meta.com/research/publications
17. Défossez A et al. Decoding speech perception from non-invasive brain recordings. Nature Machine Intelligence 5, 1097–1107 (2023). doi:10.1038/s42256-023-00714-5
18. Gaddy D, Klein D. Digital voicing of silent speech. Proc. EMNLP (2020). aclanthology.org/2020.emnlp-main.445
19. Kaifosh P, Reardon TR, CTRL-labs at Reality Labs. A generic non-invasive neuromotor interface for human–computer interaction. Nature 645 (2025). doi:10.1038/s41586-025-09255-w
20. Hochberg LR et al. Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature 485, 372–375 (2012). doi:10.1038/nature11076
21. Flesher SN et al. A brain–computer interface that evokes tactile sensations improves robotic arm control. Science 372, 831–836 (2021). doi:10.1126/science.abd0380
22. Edelman BJ et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control. Science Robotics 4, eaaw6844 (2019). doi:10.1126/scirobotics.aaw6844
23. Lorach H et al. Walking naturally after spinal cord injury using a brain–spine interface. Nature 618, 126–133 (2023). doi:10.1038/s41586-023-06094-5
24. Mitchell P et al. Assessment of safety of a fully implanted endovascular brain–computer interface for severe paralysis in 4 patients. JAMA Neurology 80, 270–278 (2023). doi:10.1001/jamaneurol.2022.4847
25. Kunz EM et al. Inner speech in motor cortex and implications for speech neuroprostheses. Cell 188 (2025). doi:10.1016/j.cell.2025.06.015
26. Kuruppu G et al. EEG foundation models: a critical review of current progress and future directions. Preprint (2025). arXiv:2507.11783
27. Liu D et al. EEG-FM-Compass: progress, benchmarking, and future directions for EEG foundation models. Preprint (2026); journal reference National Science Review. arXiv:2601.17883
28. Nunes de Oliveira JM et al. Scaling laws for EEG decoding: how much data is enough? Preprint (September 2026). arXiv:2609.35056
29. Allen EJ et al. A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nature Neuroscience 25, 116–126 (2022). doi:10.1038/s41593-021-00962-x
30. Boto E et al. Moving magnetoencephalography towards real-world applications with a wearable system. Nature 555, 657–661 (2018). doi:10.1038/nature26147
31. Yuste R et al. Four ethical priorities for neurotechnologies and AI. Nature 551, 159–163 (2017). doi:10.1038/551159a
32. UNESCO. Recommendation on the Ethics of Neurotechnology. Adopted by the General Conference, November 2025. unesco.org/en/ethics-neurotech
33. Republic of Chile. Law 21.383, amending Article 19 of the Constitution to protect mental integrity and brain activity in the face of neurotechnology (2021). bcn.cl/leychile
34. Banville H, Benchetrit Y, d’Ascoli S, Rapin J, King J-R. Scaling laws for decoding images from brain activity. Preprint (2025). arXiv:2501.15322
35. Fan C et al. Plug-and-play stability for intracortical brain–computer interfaces: a one-year demonstration of seamless brain-to-text communication. NeurIPS 36, 42258–42270 (2023). arXiv:2311.03611
36. Karpowicz BM et al. Stabilizing brain–computer interfaces through alignment of latent dynamics. Nature Communications 16, 4662 (2025). doi:10.1038/s41467-025-59652-y
37. Degenhart AD et al. Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity. Nature Biomedical Engineering 4, 672–685 (2020). doi:10.1038/s41551-020-0542-9
38. Orsborn AL et al. Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control. Neuron 82, 1380–1393 (2014). doi:10.1016/j.neuron.2014.04.048
39. Perdikis S, Tonin L, Saeedi S, Schneider C, Millán JdR. The Cybathlon BCI race: successful longitudinal mutual learning with two tetraplegic users. PLoS Biology 16, e2003787 (2018). doi:10.1371/journal.pbio.2003787
40. Azabou M et al. A unified, scalable framework for neural population decoding. NeurIPS (2023). arXiv:2310.16046
41. Jiang W-B, Zhao L-M, Lu B-L. Large brain model for learning generic representations with tremendous EEG data in BCI. ICLR (2024). arXiv:2405.18765
42. Radford A et al. Robust speech recognition via large-scale weak supervision. Preprint (2022). arXiv:2212.04356
43. Villalobos P et al. Will we run out of data? Limits of LLM scaling based on human-generated data. Preprint (2022, revised 2024). arXiv:2211.04325
44. Schrimpf M et al. The neural architecture of language: integrative modeling converges on predictive processing. PNAS 118, e2105646118 (2021). doi:10.1073/pnas.2105646118
45. Goldstein A et al. Shared computational principles for language processing in humans and deep language models. Nature Neuroscience 25, 369–380 (2022). doi:10.1038/s41593-022-01026-4
46. Li Z et al. Learning from brains how to regularize machines. NeurIPS (2019). arXiv:1911.05072
47. Moussa O, Klakow D, Toneva M. Improving semantic understanding in speech language models via brain-tuning. ICLR (2025). arXiv:2410.09230
48. Chavarriaga R, Sobolewski A, Millán JdR. Errare machinale est: the use of error-related potentials in brain–machine interfaces. Frontiers in Neuroscience 8, 208 (2014). doi:10.3389/fnins.2014.00208
49. Iturrate I et al. Teaching brain–machine interfaces as an alternative paradigm to neuroprosthetics control. Scientific Reports 5, 13893 (2015). doi:10.1038/srep13893
50. O’Sullivan JA et al. Attentional selection in a cocktail party environment can be decoded from single-trial EEG. Cerebral Cortex 25, 1697–1706 (2015). doi:10.1093/cercor/bht355
51. Ngo H-VV, Martinetz T, Born J, Mölle M. Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. Neuron 78, 545–553 (2013). doi:10.1016/j.neuron.2013.03.006
52. Biasiucci A et al. Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke. Nature Communications 9, 2421 (2018). doi:10.1038/s41467-018-04673-z
53. Cervera MA et al. Brain–computer interfaces for post-stroke motor rehabilitation: a meta-analysis. Annals of Clinical and Translational Neurology 5, 651–663 (2018). doi:10.1002/acn3.544
54. Cortese S et al. Neurofeedback for attention-deficit/hyperactivity disorder: meta-analysis of clinical and neuropsychological outcomes from randomized controlled trials. J. Am. Acad. Child Adolesc. Psychiatry 55, 444–455 (2016). doi:10.1016/j.jaac.2016.03.007
55. Dorkenwald S et al. Neuronal wiring diagram of an adult brain. Nature 634, 124–138 (2024). doi:10.1038/s41586-024-07558-y
56. Huth AG et al. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016). doi:10.1038/nature17637
57. Hebart MN et al. THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior. eLife 12, e82580 (2023). doi:10.7554/eLife.82580
58. Obeid I, Picone J. The Temple University Hospital EEG data corpus. Frontiers in Neuroscience 10, 196 (2016). doi:10.3389/fnins.2016.00196
59. Deng J et al. ImageNet: a large-scale hierarchical image database. Proc. CVPR (2009). doi:10.1109/CVPR.2009.5206848
60. Azevedo FAC et al. Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology 513, 532–541 (2009). doi:10.1002/cne.21974
61. Sutton R. The bitter lesson. Essay (2019). incompleteideas.net
62. Neurorights Foundation. Safeguarding brain data: assessing the privacy practices of consumer neurotechnology companies. Report (2024). Report (PDF)