Ethical considerations for artificial intelligence -enhanced brain-computer interface_Journal of Biomedical Engineering

Authors：

CAO Yuyu ^1,2 , XUE Yuhang ^1,2 , YANG Hengyuan ^1,2 , WANG Fan ^1,2 , LI Tianwen ^2,3 , ZHAO Lei ^2,3 ,  FU Yunfa ^1,2

1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China;
2. Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China;
3. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P. R. China;

Corresponding?author：

Keywords：

Ethics for brain-computer interface; Artificial intelligence -enhanced brain-computer interface; Brain-computer interface; Deep learning; Artificial intelligence

DOI：

10.7507/1001-5515.202507024

Video：

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Artificial intelligence-enhanced brain-computer interfaces (BCI) are expected to significantly improve the performance of traditional BCIs in multiple aspects, including usability, user experience, and user satisfaction, particularly in terms of intelligence. However, such AI-integrated or AI-based BCI systems may introduce new ethical issues. This paper first evaluated the potential of AI technology, especially deep learning, in enhancing the performance of BCI systems, including improving decoding accuracy, information transfer rate, real-time performance, and adaptability. Building on this, it was considered that AI-enhanced BCI systems might introduce new or more severe ethical issues compared to traditional BCI systems. These include the possibility of making users’ intentions and behaviors more predictable and manipulable, as well as the increased likelihood of technological abuse. The discussion also addressed measures to mitigate the ethical risks associated with these issues. It is hoped that this paper will promote a deeper understanding and reflection on the ethical risks and corresponding regulations of AI-enhanced BCIs.

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2.	Wolpaw J R, Birbaumer N, Heetderks W J, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng, 2000, 8(2): 164-173.
3.	Wolpaw J R, Wolpaw E W. Brain-computer interfaces: something new under the sun// Wolpaw J R, Wolpaw E W. Brain-computer interfaces: principles and practice. Oxford: Oxford University Press, 2012: 3-12.
4.	Ramsey N F, Millán J R. Brain-computer interfaces. Amsterdam: Elsevier, 2010: 1-16.
5.	Zhang Z, Chen Y, Zhao X, et al. A review of ethical considerations for the medical applications of brain-computer interfaces. Cogn Neurodyn, 2024, 18(6): 3603-3614.
6.	張喆, 趙旭, 馬藝昕, 等. 腦機接口技術倫理規范考量. 生物醫學工程學雜志, 2023, 40(2): 358-364.
7.	張喆, 陳衍肖, 趙旭, 等. 植入式腦機接口醫學應用倫理規范考量. 生物醫學工程學雜志, 2024, 41(1): 177-183.
8.	Bergeron D, Iorio-Morin C, Bonizzato M, et al. Use of invasive brain-computer interfaces in pediatric neurosurgery: technical and ethical considerations. J Child Neurol, 2023, 38(3): 223-238.
9.	Klein E. Ethics and the emergence of brain-computer interface medicine. Handb Clin Neurol, 2020, 168: 329-338.
10.	Burwell S, Sample M, Racine E. Ethical aspects of brain-computer interfaces: a scoping review. BMC Med Ethics, 2017, 18: 1-11.
11.	Keskinbora K H. Medical ethics considerations on artificial intelligence. J Clin Neurosci, 2019, 64: 277-282.
12.	Wang X Q, Sun H Q, Si J Y, et al. Challenges and suggestions of ethical review on clinical research involving brain-computer interfaces. Chin Med Sci J, 2024, 39(2): 131-139.
13.	呂曉彤, 丁鵬, 李思語, 等. 腦機接口人因工程及應用: 以人為中心的腦機接口設計和評價方法. 生物醫學工程學雜志, 2021, 38(2): 210-223.
14.	Lyu X, Ding P, Li S, et al. Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery. Cogn Neurodyn, 2023, 17(1): 105-118.
15.	Santos M. Research on deep learning model for real-time data processing of brain-computer interface system// 2024 International Conference on Computing, Robotics and System Sciences (ICRSS). Sanya: IEEE, 2024: 15-20.
16.	Zhang X, Ma Z, Zheng H, et al. The combination of brain-computer interfaces and artificial intelligence: applications and challenges. Ann Transl Med, 2020, 8(11): 712-722.
17.	Hu L, Zhang Z. Evolving EEG signal processing techniques in the age of artificial intelligence. Brain Sci Adv, 2020, 6(3): 159-161.
18.	Bajaj V, Sinha G R. Artificial intelligence-based brain-computer interface. Amsterdam: Elsevier, 2022: 180-205.
19.	Pan H, Ding P, Wang F, et al. Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction, and usage of online BCI systems. Front Hum Neurosci, 2024, 18: 248-257.
20.	Amin S U, Alsulaiman M, Muhammad G, et al. Deep learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. FGCS, 2019, 101: 542-554.
21.	Moses D A, Metzger S L, Liu J R, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N Engl J Med, 2021, 385(3): 217-227.
22.	Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593(7858): 249-254.
23.	Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis. Nature, 2023, 620(7976): 1031-1036.
24.	Metzger S L, Littlejohn K T, Silva A B, et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature, 2023, 620(7976): 1037-1046.
25.	Li F, He F, Wang F, et al. A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning. Appl Sci, 2020, 10(5): 354-366.
26.	Tortora S, Ghidoni S, Chisari C, et al. Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network. J Neural Eng, 2020, 17(4): 158-169.
27.	Lee D Y, Lee M, Lee S W. Decoding imagined speech based on deep metric learning for intuitive BCI communication. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1363-1374.
28.	Bu Y, Harrington D L, Lee R R, et al. Magnetoencephalogram-based brain–computer interface for hand-gesture decoding using deep learning. Cereb Cortex, 2023, 33(14): 8942-8955.
29.	Zhi H, Yu Z, Yu T, et al. A multi-domain convolutional neural network for EEG-based motor imagery decoding. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 3988-3998.
30.	Su J, An S, Wang G, et al. Transformer-based multi-scale 3D convolutional network for motor imagery classification. IEEE Sens J, 2025, 10(6): 221-229.
31.	De Venuto D, Mezzina G. A single-trial P300 detector based on symbolized EEG and autoencoded-(1D) CNN to improve ITR performance in BCIs. Sensors, 2021, 21(12): 3961-3968.
32.	Guney O B, Oblokulov M, Ozkan H. A deep neural network for SSVEP-based brain-computer interfaces. IEEE Trans Biomed Eng, 2021, 69(2): 932-944.
33.	Wang H, Pei Z, Xu L, et al. Performance enhancement of P300 detection by multiscale-CNN. IEEE Trans Instrum Meas, 2021, 70: 1-12.
34.	Rostami E, Ghassemi F, Tabanfar Z. Improving the classification of real-world SSVEP data in brain-computer interface speller systems using deep convolutional neural networks. Front Biomed Technol, 2022, 9(4): 248-254.
35.	Lampe T, Fiederer L D J, Voelker M, et al. A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping// Proceedings of the 19th International Conference on Intelligent User Interfaces. Haifa: ACM, 2014: 83-88.
36.	Roy A M. Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain-computer interface. Eng Appl Artif Intell, 2022, 116: 347-359.
37.	Nallani S V C, Ramachandran G. RLEEGNet: Integrating brain-computer interfaces with adaptive AI for intuitive responsiveness and high-accuracy motor imagery classification. arXiv, 2024: 2402.09465v1.
38.	Akuthota S, Janapati R C, Kumar K R, et al. Enhancing real-time cursor control with motor imagery and deep neural networks for brain–computer interfaces. Information, 2024, 15(11): 702-710.
39.	Xia Y, Chen J, Li J, et al. A deep-learning empowered, real-time processing platform of fNIRS/DOT for brain-computer interfaces and neurofeedback. IEEE Trans Neural Syst Rehabil Eng, 2025, 33: 1220-1230.
40.	Cao Y, Gao S, Yu H, et al. A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features. Front Physiol, 2024, 15: 548-557.
41.	Lee J, Won K, Kwon M, et al. CNN with large data achieves true zero-training in online P300 brain-computer interface. IEEE Access, 2020, 8: 74385-74400.
42.	Hosman T, Pun T K, Kapitonava A, et al. Months-long high-performance fixed LSTM decoder for cursor control in human intracortical brain-computer interfaces// 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER). Baltimore: IEEE, 2023: 1-5.
43.	Fahimi F, Zhang Z, Goh W B, et al. Towards EEG generation using GANs for BCI applications// 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Chicago: IEEE, 2019: 1-4.
44.	Jin C, Song A H, Kim S E. Two-phase multitask autoencoder-based deep learning framework for subject-independent EEG motor imagery classification. IEEE Access, 2024, 12: 77356-77367.
45.	Zhang J, Li K, Yang B, et al. Local and global convolutional transformer-based motor imagery EEG classification. Front Neurosc, 2023, 17: 1219-1228.
46.	Xia K, Duch W, Sun Y, et al. Privacy-preserving brain–computer interfaces: A systematic review. IEEE Trans Comput Soc Syst, 2022, 10(5): 2312-2324.
47.	Naufel S, Klein E. Brain–computer interface (BCI) researcher perspectives on neural data ownership and privacy. J Neural Eng, 2020, 17(1): 156-167.
48.	Lyreskog D M, Zohny H, Singh I, et al. The ethics of thinking with machines: Brain-computer interfaces in the era of artificial intelligence. Int J Chin Comp Philos Med, 2023, 21(2): 514-522.
49.	Cao Z. A review of artificial intelligence for EEG‐based brain? computer interfaces and applications. BSA, 2020, 6(3): 162-170.
50.	Klein E, Nam C S. Neuroethics and brain-computer interfaces (BCIs). Brain-Comp Inter, 2016, 3(3): 123-125.
51.	Cassinadri G, Ienca M. Non-voluntary BCI explantation: assessing possible neurorights violations in light of contrasting mental ontologies. J Med Ethics, 2024, 40(3): 226-235.
52.	Ayyalasomayajula M M T. Ethical considerations in AI implementation for neurodegenerative disease diagnosis and treatment// Integrating Neuroimaging, Computational Neuroscience, and Artificial Intelligence. Boca Raton: CRC Press, 2025: 105-127.
53.	Onciul R, Tataru C I, Dumitru A V, et al. Artificial intelligence and neuroscience: transformative synergies in brain research and clinical applications. J Clin Med, 2025, 14(2): 550-559.
54.	Ma W, Ma T, Organisciak D, et al. The progress and prospects of data capital for zero-shot deep brain–computer interfaces. Electronics, 2025, 14(3): 508-516.
55.	Liu M, Ning Y, Teixayavong S, et al. A translational perspective towards clinical AI fairness. NPJ Digit Med, 2023, 6(1): 172-181.
56.	Guan J. Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance. Chin Med Sci J, 2019, 34(2): 76-83.
57.	V?rbu K, Muhammad N, Muhammad Y. Past, present, and future of EEG-based BCI applications. Sensors, 2022, 22(9): 3331-3340.
58.	Elashmawi W H, Ayman A, Antoun M, et al. A comprehensive review on brain–computer interface (BCI)-based machine and deep learning algorithms for stroke rehabilitation. Appl Sci, 2024, 14(14): 6347-6357.
59.	Dinker N. Artificial intelligence and inequality: examining the social divides created by technological advancements. IJISEM, 2024, 15(8): 228-236.
60.	Islam M, Vashishat A, Kumar M. Advancements beyond limb loss: exploring the intersection of AI and BCI in prosthetic evaluation. Curr Pharm Des, 2024, 30(35): 2749-2752.
61.	Zhang H. Neurotechnology and ethics: Reflections on brain-computer interface technology. Sci Prog Hum, 2025, 1(1): 14-19.
62.	Phillips V. From neurons to networks: ethical dimensions of AI-infused neural interfaces. AI & Ethics, 2025, 5: 3531-3536.
63.	Zeng Y, Sun K, Lu E. Declaration on the ethics of brain–computer interfaces and augment intelligence. AI & Ethics, 2021, 1(3): 209-211.
64.	Conrad C, Heggie C. Legal and ethical challenges raised by advances in brain-computer interface technology. Can J Law Tech, 2024, 21(2): 201-211.
65.	Yang H, Jiang L. Regulating neural data processing in the age of BCIs: ethical concerns and legal approaches. Digit Health, 2025, 11: 456-467.

1. Graimann B, Allison B Z, Pfurtscheller G. Brain-computer interfaces: Revolutionizing human-computer interaction. Berlin: Springer Science & Business Media, 2010: 2-18.
2. Wolpaw J R, Birbaumer N, Heetderks W J, et al. Brain-computer interface technology: a review of the first international meeting. IEEE Trans Rehabil Eng, 2000, 8(2): 164-173.
3. Wolpaw J R, Wolpaw E W. Brain-computer interfaces: something new under the sun// Wolpaw J R, Wolpaw E W. Brain-computer interfaces: principles and practice. Oxford: Oxford University Press, 2012: 3-12.
4. Ramsey N F, Millán J R. Brain-computer interfaces. Amsterdam: Elsevier, 2010: 1-16.
5. Zhang Z, Chen Y, Zhao X, et al. A review of ethical considerations for the medical applications of brain-computer interfaces. Cogn Neurodyn, 2024, 18(6): 3603-3614.
6. 張喆, 趙旭, 馬藝昕, 等. 腦機接口技術倫理規范考量. 生物醫學工程學雜志, 2023, 40(2): 358-364.
7. 張喆, 陳衍肖, 趙旭, 等. 植入式腦機接口醫學應用倫理規范考量. 生物醫學工程學雜志, 2024, 41(1): 177-183.
8. Bergeron D, Iorio-Morin C, Bonizzato M, et al. Use of invasive brain-computer interfaces in pediatric neurosurgery: technical and ethical considerations. J Child Neurol, 2023, 38(3): 223-238.
9. Klein E. Ethics and the emergence of brain-computer interface medicine. Handb Clin Neurol, 2020, 168: 329-338.
10. Burwell S, Sample M, Racine E. Ethical aspects of brain-computer interfaces: a scoping review. BMC Med Ethics, 2017, 18: 1-11.
11. Keskinbora K H. Medical ethics considerations on artificial intelligence. J Clin Neurosci, 2019, 64: 277-282.
12. Wang X Q, Sun H Q, Si J Y, et al. Challenges and suggestions of ethical review on clinical research involving brain-computer interfaces. Chin Med Sci J, 2024, 39(2): 131-139.
13. 呂曉彤, 丁鵬, 李思語, 等. 腦機接口人因工程及應用: 以人為中心的腦機接口設計和評價方法. 生物醫學工程學雜志, 2021, 38(2): 210-223.
14. Lyu X, Ding P, Li S, et al. Human factors engineering of BCI: an evaluation for satisfaction of BCI based on motor imagery. Cogn Neurodyn, 2023, 17(1): 105-118.
15. Santos M. Research on deep learning model for real-time data processing of brain-computer interface system// 2024 International Conference on Computing, Robotics and System Sciences (ICRSS). Sanya: IEEE, 2024: 15-20.
16. Zhang X, Ma Z, Zheng H, et al. The combination of brain-computer interfaces and artificial intelligence: applications and challenges. Ann Transl Med, 2020, 8(11): 712-722.
17. Hu L, Zhang Z. Evolving EEG signal processing techniques in the age of artificial intelligence. Brain Sci Adv, 2020, 6(3): 159-161.
18. Bajaj V, Sinha G R. Artificial intelligence-based brain-computer interface. Amsterdam: Elsevier, 2022: 180-205.
19. Pan H, Ding P, Wang F, et al. Comprehensive evaluation methods for translating BCI into practical applications: usability, user satisfaction, and usage of online BCI systems. Front Hum Neurosci, 2024, 18: 248-257.
20. Amin S U, Alsulaiman M, Muhammad G, et al. Deep learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. FGCS, 2019, 101: 542-554.
21. Moses D A, Metzger S L, Liu J R, et al. Neuroprosthesis for decoding speech in a paralyzed person with anarthria. N Engl J Med, 2021, 385(3): 217-227.
22. Willett F R, Avansino D T, Hochberg L R, et al. High-performance brain-to-text communication via handwriting. Nature, 2021, 593(7858): 249-254.
23. Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis. Nature, 2023, 620(7976): 1031-1036.
24. Metzger S L, Littlejohn K T, Silva A B, et al. A high-performance neuroprosthesis for speech decoding and avatar control. Nature, 2023, 620(7976): 1037-1046.
25. Li F, He F, Wang F, et al. A novel simplified convolutional neural network classification algorithm of motor imagery EEG signals based on deep learning. Appl Sci, 2020, 10(5): 354-366.
26. Tortora S, Ghidoni S, Chisari C, et al. Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network. J Neural Eng, 2020, 17(4): 158-169.
27. Lee D Y, Lee M, Lee S W. Decoding imagined speech based on deep metric learning for intuitive BCI communication. IEEE Trans Neural Syst Rehabil Eng, 2021, 29: 1363-1374.
28. Bu Y, Harrington D L, Lee R R, et al. Magnetoencephalogram-based brain–computer interface for hand-gesture decoding using deep learning. Cereb Cortex, 2023, 33(14): 8942-8955.
29. Zhi H, Yu Z, Yu T, et al. A multi-domain convolutional neural network for EEG-based motor imagery decoding. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 3988-3998.
30. Su J, An S, Wang G, et al. Transformer-based multi-scale 3D convolutional network for motor imagery classification. IEEE Sens J, 2025, 10(6): 221-229.
31. De Venuto D, Mezzina G. A single-trial P300 detector based on symbolized EEG and autoencoded-(1D) CNN to improve ITR performance in BCIs. Sensors, 2021, 21(12): 3961-3968.
32. Guney O B, Oblokulov M, Ozkan H. A deep neural network for SSVEP-based brain-computer interfaces. IEEE Trans Biomed Eng, 2021, 69(2): 932-944.
33. Wang H, Pei Z, Xu L, et al. Performance enhancement of P300 detection by multiscale-CNN. IEEE Trans Instrum Meas, 2021, 70: 1-12.
34. Rostami E, Ghassemi F, Tabanfar Z. Improving the classification of real-world SSVEP data in brain-computer interface speller systems using deep convolutional neural networks. Front Biomed Technol, 2022, 9(4): 248-254.
35. Lampe T, Fiederer L D J, Voelker M, et al. A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping// Proceedings of the 19th International Conference on Intelligent User Interfaces. Haifa: ACM, 2014: 83-88.
36. Roy A M. Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain-computer interface. Eng Appl Artif Intell, 2022, 116: 347-359.
37. Nallani S V C, Ramachandran G. RLEEGNet: Integrating brain-computer interfaces with adaptive AI for intuitive responsiveness and high-accuracy motor imagery classification. arXiv, 2024: 2402.09465v1.
38. Akuthota S, Janapati R C, Kumar K R, et al. Enhancing real-time cursor control with motor imagery and deep neural networks for brain–computer interfaces. Information, 2024, 15(11): 702-710.
39. Xia Y, Chen J, Li J, et al. A deep-learning empowered, real-time processing platform of fNIRS/DOT for brain-computer interfaces and neurofeedback. IEEE Trans Neural Syst Rehabil Eng, 2025, 33: 1220-1230.
40. Cao Y, Gao S, Yu H, et al. A motor imagery classification model based on hybrid brain-computer interface and multitask learning of electroencephalographic and electromyographic deep features. Front Physiol, 2024, 15: 548-557.
41. Lee J, Won K, Kwon M, et al. CNN with large data achieves true zero-training in online P300 brain-computer interface. IEEE Access, 2020, 8: 74385-74400.
42. Hosman T, Pun T K, Kapitonava A, et al. Months-long high-performance fixed LSTM decoder for cursor control in human intracortical brain-computer interfaces// 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER). Baltimore: IEEE, 2023: 1-5.
43. Fahimi F, Zhang Z, Goh W B, et al. Towards EEG generation using GANs for BCI applications// 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Chicago: IEEE, 2019: 1-4.
44. Jin C, Song A H, Kim S E. Two-phase multitask autoencoder-based deep learning framework for subject-independent EEG motor imagery classification. IEEE Access, 2024, 12: 77356-77367.
45. Zhang J, Li K, Yang B, et al. Local and global convolutional transformer-based motor imagery EEG classification. Front Neurosc, 2023, 17: 1219-1228.
46. Xia K, Duch W, Sun Y, et al. Privacy-preserving brain–computer interfaces: A systematic review. IEEE Trans Comput Soc Syst, 2022, 10(5): 2312-2324.
47. Naufel S, Klein E. Brain–computer interface (BCI) researcher perspectives on neural data ownership and privacy. J Neural Eng, 2020, 17(1): 156-167.
48. Lyreskog D M, Zohny H, Singh I, et al. The ethics of thinking with machines: Brain-computer interfaces in the era of artificial intelligence. Int J Chin Comp Philos Med, 2023, 21(2): 514-522.
49. Cao Z. A review of artificial intelligence for EEG‐based brain? computer interfaces and applications. BSA, 2020, 6(3): 162-170.
50. Klein E, Nam C S. Neuroethics and brain-computer interfaces (BCIs). Brain-Comp Inter, 2016, 3(3): 123-125.
51. Cassinadri G, Ienca M. Non-voluntary BCI explantation: assessing possible neurorights violations in light of contrasting mental ontologies. J Med Ethics, 2024, 40(3): 226-235.
52. Ayyalasomayajula M M T. Ethical considerations in AI implementation for neurodegenerative disease diagnosis and treatment// Integrating Neuroimaging, Computational Neuroscience, and Artificial Intelligence. Boca Raton: CRC Press, 2025: 105-127.
53. Onciul R, Tataru C I, Dumitru A V, et al. Artificial intelligence and neuroscience: transformative synergies in brain research and clinical applications. J Clin Med, 2025, 14(2): 550-559.
54. Ma W, Ma T, Organisciak D, et al. The progress and prospects of data capital for zero-shot deep brain–computer interfaces. Electronics, 2025, 14(3): 508-516.
55. Liu M, Ning Y, Teixayavong S, et al. A translational perspective towards clinical AI fairness. NPJ Digit Med, 2023, 6(1): 172-181.
56. Guan J. Artificial intelligence in healthcare and medicine: promises, ethical challenges and governance. Chin Med Sci J, 2019, 34(2): 76-83.
57. V?rbu K, Muhammad N, Muhammad Y. Past, present, and future of EEG-based BCI applications. Sensors, 2022, 22(9): 3331-3340.
58. Elashmawi W H, Ayman A, Antoun M, et al. A comprehensive review on brain–computer interface (BCI)-based machine and deep learning algorithms for stroke rehabilitation. Appl Sci, 2024, 14(14): 6347-6357.
59. Dinker N. Artificial intelligence and inequality: examining the social divides created by technological advancements. IJISEM, 2024, 15(8): 228-236.
60. Islam M, Vashishat A, Kumar M. Advancements beyond limb loss: exploring the intersection of AI and BCI in prosthetic evaluation. Curr Pharm Des, 2024, 30(35): 2749-2752.
61. Zhang H. Neurotechnology and ethics: Reflections on brain-computer interface technology. Sci Prog Hum, 2025, 1(1): 14-19.
62. Phillips V. From neurons to networks: ethical dimensions of AI-infused neural interfaces. AI & Ethics, 2025, 5: 3531-3536.
63. Zeng Y, Sun K, Lu E. Declaration on the ethics of brain–computer interfaces and augment intelligence. AI & Ethics, 2021, 1(3): 209-211.
64. Conrad C, Heggie C. Legal and ethical challenges raised by advances in brain-computer interface technology. Can J Law Tech, 2024, 21(2): 201-211.
65. Yang H, Jiang L. Regulating neural data processing in the age of BCIs: ethical concerns and legal approaches. Digit Health, 2025, 11: 456-467.

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