Academic Journal
[Analysis of the constraints of inherent limitations on the capability boundaries of brain-computer interfaces and corresponding strategies].
| Τίτλος: | [Analysis of the constraints of inherent limitations on the capability boundaries of brain-computer interfaces and corresponding strategies]. |
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| Συγγραφείς: | Fu Y; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.; Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China., Yao H; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.; Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China., Li T; Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China.; Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P. R. China., Zhao L; Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China.; Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P. R. China., Yang X; Department of Rehabilitation Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming 650500, China.; Neurorehabilitation Team, Kunming Medical University, Kunming 650500, P. R. China., Luo R; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China.; Brain Cognition and Brain-computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P. R. China., Xu J; Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China. |
| Πηγή: | Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi [Sheng Wu Yi Xue Gong Cheng Xue Za Zhi] 2026 Aug 25; Vol. 43 (4), pp. 669-677. |
| Τύπος έκδοσης: | Journal Article; Review; English Abstract |
| Γλώσσα: | Chinese |
| Στοιχεία περιοδικού: | Publisher: Sichuan Sheng sheng wu yi xue gong cheng xue hui Country of Publication: China NLM ID: 9426398 Publication Model: Print Cited Medium: Print ISSN: 1001-5515 (Print) Linking ISSN: 10015515 NLM ISO Abbreviation: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi Subsets: MEDLINE |
| Imprint Name(s): | Publication: Chengdu : Sichuan Sheng sheng wu yi xue gong cheng xue hui Original Publication: Chengdu : Sichuan Sheng sheng wu yi xue gong cheng xue hui : Hua xi yi ke da xue : Chengdu ke ji da xue |
| Ιατρικοί όροι (MeSH): | Brain*/physiology , Brain-Computer Interfaces* , Signal Processing, Computer-Assisted*, Humans ; Electroencephalography ; Algorithms ; Signal-To-Noise Ratio |
| Περίληψη: | 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. |
| References: | Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2024 Feb 25;41(1):177-183. (PMID: 38403619) Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Jun 25;42(3):431-437. (PMID: 40566763) Biomimetics (Basel). 2026 Feb 22;11(2):. (PMID: 41744603) Brain Inform. 2023 Aug 4;10(1):20. (PMID: 37540385) IEEE Trans Neural Syst Rehabil Eng. 2023;31:710-719. (PMID: 37015413) Front Hum Neurosci. 2024 Jun 05;18:1429130. (PMID: 38903409) Clin Neurophysiol. 2002 Jun;113(6):767-91. (PMID: 12048038) Nature. 2023 Jun;618(7963):126-133. (PMID: 37225984) Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2026 Apr 25;43(2):414-420. (PMID: 42037346) J Neural Eng. 2015 Aug;12(4):046008. (PMID: 26035476) Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Oct 25;42(5):1085-1091. (PMID: 41152182) Nature. 2023 Aug;620(7976):1037-1046. (PMID: 37612505) Front Neurosci. 2024 Jan 15;17:1345961. (PMID: 38287988) Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2025 Aug 25;42(4):651-659. (PMID: 40887178) N Engl J Med. 2024 Aug 15;391(7):609-618. (PMID: 39141853) Nature. 2023 Aug;620(7976):1031-1036. (PMID: 37612500) IEEE Trans Biomed Eng. 2018 Jan;65(1):104-112. (PMID: 28436836) J Neural Eng. 2018 Oct;15(5):056013. (PMID: 29932424) Nature. 2017 Nov 8;551(7679):159-163. (PMID: 29120438) Cogn Neurodyn. 2025 Dec;19(1):130. (PMID: 40843107) Front Neurosci. 2024 Aug 05;18:1449208. (PMID: 39161655) Brain Topogr. 2024 Nov;37(6):1118-1138. (PMID: 38990422) Brain Topogr. 2010 Jun;23(2):194-8. (PMID: 19946737) Front Hum Neurosci. 2024 Mar 27;18:1391550. (PMID: 38601800) Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2023 Apr 25;40(2):358-364. (PMID: 37139769) |
| Contributed Indexing: | Keywords: Brain-computer interface; Capability boundaries; Inherent limitations; Neural information constraints; Paradigm dependence Local Abstract: [Publisher, Chinese] 脑机接口（BCI）近年来发展迅速，但对其固有局限与能力边界的系统认识仍相对不足。本文从神经信息生成、表达、获取与利用全过程出发，分析BCI固有局限对能力边界形成的约束机制及其应对策略。分析表明，BCI固有局限主要源于神经编码动态性、个体差异、低信噪比、部分可观测性及范式依赖性等约束，并作为能力边界形成的内在基础，共同约束神经信息层、人因层和系统层能力边界，表现为准确率、信息传输率、复杂意图解码、用户体验以及稳定性、可靠性和安全性等方面的性能上限。针对不同层级约束，本文总结了信息增强、自适应解码、人机协同和系统优化等策略。分析认为，BCI性能提升本质上是在既有约束条件下提高信息利用效率，提升可实现性能并拓展能力边界，而非突破其根本限制。本文可为BCI研究、技术创新、应用及科学传播提供参考。. |
| Entry Date(s): | Date Created: 20260827 Date Completed: 20260827 Latest Revision: 20260829 |
| Update Code: | 20260829 |
| PubMed Central ID: | PMC13519809 |
| DOI: | 10.7507/1001-5515.202603080 |
| PMID: | 42656097 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1001-5515 |
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| DOI: | 10.7507/1001-5515.202603080 |