Analysis of the constraints of inherent limitations on the capability boundaries of brain-computer interfaces and corresponding strategies_Journal of Biomedical Engineering

Authors：

 FU Yunfa ^1,2 , YAO Haoyu ^1,2 , LI Tianwen ^2,3 , ZHAO Lei ^2,3 , YANG Xue ^4,5 , LUO Rongzhang ^1,2 ,  XU Jiaping ¹

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;
4. Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650500, China;
5. Neurorehabilitation Team, Kunming Medical University, Kunming 650500, P. R. China;

Corresponding?author：

FU Yunfa, Email: fyf@ynu.edu.cn; XU Jiaping, Email: 2395000438@qq.com

Keywords：

Brain-computer interface; Inherent limitations; Capability boundaries; Neural information constraints; Paradigm dependence

DOI：

10.7507/1001-5515.202603080

Video：

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Brain-computer interfaces (BCIs) has developed rapidly in recent years, yet a systematic understanding of its inherent limitations and capability boundaries remains insufficient. This paper analyzes the constraint mechanisms through which inherent limitations shape BCI capability boundaries and discusses corresponding strategies from the perspectives of neural information generation, representation, acquisition, and utilization. The analysis indicates that the inherent limitations of BCI mainly arise from the dynamic nature of neural coding, inter-individual variability, low signal-to-noise ratios, partial observability, and paradigm dependence. As the intrinsic basis for capability boundary formation, these limitations jointly constrain the capability boundaries at the neural information, human-factors, and system levels, which are manifested as the upper performance limits of decoding accuracy, information transfer rate, complex intention decoding, user experience, as well as system stability, reliability, and safety. To address these constraints, this paper summarizes representative strategies, including information enhancement, adaptive decoding, human-machine collaboration, and system optimization. The analysis suggests that improvements in BCI performance fundamentally rely on enhancing neural information utilization and progressively expanding achievable capability boundaries under existing constraints, rather than overcoming their underlying limitations. The proposed framework provides a theoretical basis for understanding the relationship between inherent limitations and capability boundaries, and provides a reference for BCI research, technological innovation, practical applications, and scientific communication.

Citation： FU Yunfa, YAO Haoyu, LI Tianwen, ZHAO Lei, YANG Xue, LUO Rongzhang, XU Jiaping. Analysis of the constraints of inherent limitations on the capability boundaries of brain-computer interfaces and corresponding strategies. Journal of Biomedical Engineering, 2026, 43(4): 669-677. doi: 10.7507/1001-5515.202603080 Copy

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4.	Allison B Z, Chavarriaga R, Wolpaw J R. Brain-computer interfaces: expectation versus reality[J]. Curr Opin Biomed Eng, 2023, 27: 100471.
5.	Chen Y, Wang F, Li T, et al. Considerations and discussions on the clear definition and definite scope of brain-computer interfaces[J]. Front Neurosci, 2024, 18: 1449208.
6.	Chen Y, Wang F, Li T, et al. Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces[J]. Front Hum Neurosci, 2024, 18: 1391550.
7.	伏云發, 魯海晨. 腦機接口(BCI)的技術成熟度與泡沫風險分析: 從研究到產業轉化的考量[J]. 生物醫學工程學雜志, 2025, 42(4): 651-659.
8.	Tai P, Ding P, Wang F, et al. Brain-computer interface paradigms and neural coding[J]. Front Neurosci, 2024, 17: 1345961.
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10.	Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for high-speed brain speller using task-related component analysis[J]. IEEE Trans Biomed Eng, 2018, 65(1): 104-112.
11.	王東慶, 周建華, 伏云發. EWT算法在單通道腦電信號眼電偽跡自動去除中的研究[J]. 電子測量與儀器學報, 2023, 37(2): 121-129.
12.	劉光明. 腦機接口替代身體“限度”的哲學審視[J]. 醫學與哲學, 2024, 45(9): 11-15.
13.	Zhao L, Liu Y, Gao J, et al. Visual imagery-based brain-computer interaction paradigms and neural encoding and decoding[J]. IEEE Trans Hum Mach Syst, 2025, 55(3): 358-371.
14.	Ding P, Wang F, Zhao L, et al. HWI encoding/decoding of a non-invasive HWI-BCI paradigm based on temporal variation abundance scale[J]. Cogn Neurodyn, 2025, 19(1): 130.
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18.	Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based BCI[J]. J Neural Eng, 2015, 12(4): 046008.
19.	邱文燦, 馬良, 郭浩月, 等. 綜述與專論: 植入式腦機接口系統實現、臨床進展與技術挑戰[J]. 生物化學與生物物理進展, 2024, 51(10): 2478-2497.
20.	Maiseli B, Abdalla A T, Massawe L V, et al. Brain-computer interface: trend, challenges, and threats[J]. Brain Inform, 2023, 10(1): 20.
21.	伏云發, 王帆, 丁鵬, 等. 腦-計算機接口[M]. 北京: 國防工業出版社, 2023: 631-646.
22.	Vidaurre C, Blankertz B. Towards a cure for BCI illiteracy[J]. Brain Topogr, 2010, 23(2): 194-198.
23.	Wu D, Xu Y, Lu B L. Transfer learning for EEG-based brain-computer interfaces: a review of progress made since 2016[J]. IEEE Trans Cogn Dev Syst, 2022, 14(1): 4-19.
24.	Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis[J]. Nature, 2023, 620(7976): 1031-1036.
25.	Metzger S L, Littlejohn K T, Silva A B, et al. A high-performance neuroprosthesis for speech decoding and avatar control[J]. Nature, 2023, 620(7976): 1037-1046.
26.	Card N S, Wairagkar M, Iacobacci C, et al. An accurate and rapidly calibrating speech neuroprosthesis[J]. N Engl J Med, 2024, 391(7): 609-618.
27.	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[J]. Front Hum Neurosci, 2024, 18: 1429130.
28.	Galán F, Nuttin M, Lew E, et al. A shared-control approach to an EEG-based brain-computer interface for a motorized wheelchair[J]. IEEE Trans Biomed Eng, 2008, 55(7): 1814-1822.
29.	潘鶴, 丁鵬, 王帆, 等. 雙向閉環運動想象腦機接口主動康復訓練系統的康復功效評價方法[J]. 生物醫學工程學雜志, 2025, 42(3): 431-437.
30.	曹育育, 薛雨航, 楊恒源, 等. 人工智能增強的腦機接口倫理規范考量[J]. 生物醫學工程學雜志, 2025, 42(5): 1085-1091.
31.	薛雨航, 李志豪, 王帆, 等. 神經反饋技術倫理風險及規范考量[J]. 生物醫學工程學雜志, 2026, 43(2): 414-420.
32.	張喆, 趙旭, 馬藝昕, 等. 腦機接口技術倫理規范考量[J]. 生物醫學工程學雜志, 2023, 40(2): 358-364.
33.	Wang Y, Ge M, Xu S. Advances in brain-computer interfaces (BCI): challenges and opportunities[J]. Biomimetics, 2026, 11(2): 157.
34.	Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spinal cord injury using a brain-spine interface[J]. Nature, 2023, 618(7963): 126-133.
35.	Wang F, Wang P, Ding P, et al. An innovative integrated processing framework for enhanced decoding of mental-imagery MEG signals[J]. Biomed Signal Process Control, 2025, 109: 108068.
36.	Fu Y, Xue Y, Chen X, et al. Brain-computer interface (BCI) in clinical neurorestorative practices[J]. J Neurorestoratology, 2025, 13(2): 100188.
37.	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[J]. 生物醫學工程學雜志, 2024, 41(1): 177-183.
38.	Yuste R, Goering S, Agüera Y Arcas B, et al. Four ethical priorities for neurotechnologies and AI[J]. Nature, 2017, 551(7679): 159-163.

1. 伏云發, 程天驕, 羅榮章, 等. 腦機接口(BCI)的科學定義——必要組件、基本特征、能力邊界與范疇界定[J]. 生物醫學工程學雜志, 2026, 43(1): 1-7.
2. Wolpaw J R, Birbaumer N, McFarland D J, et al. Brain-computer interfaces for communication and control[J]. Clin Neurophysiol, 2002, 113(6): 767-791.
3. Gao X, Wang Y, Chen X, et al. Brain-computer interface—a brain-in-the-loop communication system[J]. Proc IEEE, 2025, 113(5): 478-511.
4. Allison B Z, Chavarriaga R, Wolpaw J R. Brain-computer interfaces: expectation versus reality[J]. Curr Opin Biomed Eng, 2023, 27: 100471.
5. Chen Y, Wang F, Li T, et al. Considerations and discussions on the clear definition and definite scope of brain-computer interfaces[J]. Front Neurosci, 2024, 18: 1449208.
6. Chen Y, Wang F, Li T, et al. Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces[J]. Front Hum Neurosci, 2024, 18: 1391550.
7. 伏云發, 魯海晨. 腦機接口(BCI)的技術成熟度與泡沫風險分析: 從研究到產業轉化的考量[J]. 生物醫學工程學雜志, 2025, 42(4): 651-659.
8. Tai P, Ding P, Wang F, et al. Brain-computer interface paradigms and neural coding[J]. Front Neurosci, 2024, 17: 1345961.
9. Xu M, Han J, Ming D, et al. Advances in steady-state visually evoked potential-based brain-computer interfaces: a technology review[J]. J Neural Eng, 2023, 20(2): 021003.
10. Nakanishi M, Wang Y, Chen X, et al. Enhancing detection of SSVEPs for high-speed brain speller using task-related component analysis[J]. IEEE Trans Biomed Eng, 2018, 65(1): 104-112.
11. 王東慶, 周建華, 伏云發. EWT算法在單通道腦電信號眼電偽跡自動去除中的研究[J]. 電子測量與儀器學報, 2023, 37(2): 121-129.
12. 劉光明. 腦機接口替代身體“限度”的哲學審視[J]. 醫學與哲學, 2024, 45(9): 11-15.
13. Zhao L, Liu Y, Gao J, et al. Visual imagery-based brain-computer interaction paradigms and neural encoding and decoding[J]. IEEE Trans Hum Mach Syst, 2025, 55(3): 358-371.
14. Ding P, Wang F, Zhao L, et al. HWI encoding/decoding of a non-invasive HWI-BCI paradigm based on temporal variation abundance scale[J]. Cogn Neurodyn, 2025, 19(1): 130.
15. Man H, Gong A, Song X, et al. Decoding the preparation stage of target shooting under audiovisual restricted conditions: investigating neural mechanisms using microstate analysis[J]. Brain Topogr, 2024, 37(6): 1118-1138.
16. Lawhern V J, Solon A J, Waytowich N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces[J]. J Neural Eng, 2018, 15(5): 056013.
17. Song Y, Zheng Q, Liu B, et al. EEG conformer: convolutional transformer for EEG decoding and visualization[J]. IEEE Trans Neural Syst Rehabil Eng, 2023, 31: 710-719.
18. Chen X, Wang Y, Gao S, et al. Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based BCI[J]. J Neural Eng, 2015, 12(4): 046008.
19. 邱文燦, 馬良, 郭浩月, 等. 綜述與專論: 植入式腦機接口系統實現、臨床進展與技術挑戰[J]. 生物化學與生物物理進展, 2024, 51(10): 2478-2497.
20. Maiseli B, Abdalla A T, Massawe L V, et al. Brain-computer interface: trend, challenges, and threats[J]. Brain Inform, 2023, 10(1): 20.
21. 伏云發, 王帆, 丁鵬, 等. 腦-計算機接口[M]. 北京: 國防工業出版社, 2023: 631-646.
22. Vidaurre C, Blankertz B. Towards a cure for BCI illiteracy[J]. Brain Topogr, 2010, 23(2): 194-198.
23. Wu D, Xu Y, Lu B L. Transfer learning for EEG-based brain-computer interfaces: a review of progress made since 2016[J]. IEEE Trans Cogn Dev Syst, 2022, 14(1): 4-19.
24. Willett F R, Kunz E M, Fan C, et al. A high-performance speech neuroprosthesis[J]. Nature, 2023, 620(7976): 1031-1036.
25. Metzger S L, Littlejohn K T, Silva A B, et al. A high-performance neuroprosthesis for speech decoding and avatar control[J]. Nature, 2023, 620(7976): 1037-1046.
26. Card N S, Wairagkar M, Iacobacci C, et al. An accurate and rapidly calibrating speech neuroprosthesis[J]. N Engl J Med, 2024, 391(7): 609-618.
27. 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[J]. Front Hum Neurosci, 2024, 18: 1429130.
28. Galán F, Nuttin M, Lew E, et al. A shared-control approach to an EEG-based brain-computer interface for a motorized wheelchair[J]. IEEE Trans Biomed Eng, 2008, 55(7): 1814-1822.
29. 潘鶴, 丁鵬, 王帆, 等. 雙向閉環運動想象腦機接口主動康復訓練系統的康復功效評價方法[J]. 生物醫學工程學雜志, 2025, 42(3): 431-437.
30. 曹育育, 薛雨航, 楊恒源, 等. 人工智能增強的腦機接口倫理規范考量[J]. 生物醫學工程學雜志, 2025, 42(5): 1085-1091.
31. 薛雨航, 李志豪, 王帆, 等. 神經反饋技術倫理風險及規范考量[J]. 生物醫學工程學雜志, 2026, 43(2): 414-420.
32. 張喆, 趙旭, 馬藝昕, 等. 腦機接口技術倫理規范考量[J]. 生物醫學工程學雜志, 2023, 40(2): 358-364.
33. Wang Y, Ge M, Xu S. Advances in brain-computer interfaces (BCI): challenges and opportunities[J]. Biomimetics, 2026, 11(2): 157.
34. Lorach H, Galvez A, Spagnolo V, et al. Walking naturally after spinal cord injury using a brain-spine interface[J]. Nature, 2023, 618(7963): 126-133.
35. Wang F, Wang P, Ding P, et al. An innovative integrated processing framework for enhanced decoding of mental-imagery MEG signals[J]. Biomed Signal Process Control, 2025, 109: 108068.
36. Fu Y, Xue Y, Chen X, et al. Brain-computer interface (BCI) in clinical neurorestorative practices[J]. J Neurorestoratology, 2025, 13(2): 100188.
37. 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[J]. 生物醫學工程學雜志, 2024, 41(1): 177-183.
38. Yuste R, Goering S, Agüera Y Arcas B, et al. Four ethical priorities for neurotechnologies and AI[J]. Nature, 2017, 551(7679): 159-163.

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