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Article(id=1324320302301323878, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1324320221309313194, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2025.07.00039, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1751817600000, receivedDateStr=2025-07-07, revisedDate=1768233600000, revisedDateStr=2026-01-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1793672164750, onlineDateStr=2026-11-03, pubDate=1790524800000, pubDateStr=2026-09-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793672164750, onlineIssueDateStr=2026-11-03, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793672164750, creator=13701087609, updateTime=1793672164750, updator=13701087609, issue=Issue{id=1324320221309313194, tenantId=1146029695717560320, journalId=1146031591421210625, year='2026', volume='44', issue='18', pageStart='1', pageEnd='180', issueExtLink='null', onlineDate='null', pubDate='1790524800000', pubDateStr='2026-09-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1793672145439, creator='13701087609', updateTime=1793673806843, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324327189847605469, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1324320221309313194, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324327189847605470, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1324320221309313194, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=169, endPage=174, ext={EN=ArticleExt(id=1324320302653645415, articleId=1324320302301323878, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Focus on the innovative development of brain−computer interface and cultivate new quality productivity, columnId=1150494643415773491, journalTitle=Science & Technology Review, columnName=Policy Forum, runingTitle=null, highlight=null, articleAbstract=

Brain−computer interface (BCI), as a cutting−edge field integrating brain science, artificial intelligence, and engineering technology, are becoming an important vehicle for cultivating new types of productivity. This study focuses on the crucial stage of BCI's transition from scientific research to industrial application, systematically reviewing our country's latest progress in policy layout, basic components, encoding and decoding methods, and typical application systems. It proposes that BCIs not only provide a path for the accumulation of new production factors through the efficient collection and processing of brain data, but also promote the formation of emerging industrial chains represented by personalized diagnosis and treatment of brain diseases, and demonstrate forward−looking potential in the field of human−machine collaborative brain−computer intelligence. Furthermore, this study proposes development suggestions from the dimensions of technological autonomy, industry−academia−research integration, and ethical standards, aiming to provide theoretical reference and analytical framework for our country to seize the strategic high ground in BCIs and shape its future industrial competitiveness.

, authors=Xiaogang CHEN1, Jingjing CHEN2, Nanlin SHI3, Xiaorong GAO3, *, authorsList=Xiaogang CHEN, Jingjing CHEN, Nanlin SHI, Xiaorong GAO, authorCompany=null, correspAuthors=Xiaorong GAO, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1324320305132479087, articleId=1324320302301323878, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=聚焦脑机接口创新发展，培育新质生产力, columnId=1150494643549991220, journalTitle=科技导报, columnName=政策建议, runingTitle=null, highlight=null, articleAbstract=

脑机接口作为融合脑科学、人工智能与工程技术的前沿方向，正成为培育新质生产力的重要载体。立足于脑机接口从科学研究走向产业落地的关键阶段，梳理了中国在政策布局、基础器件、编解码方法与典型应用系统等方面的最新进展，提出脑机接口不仅通过高效采集与处理脑数据为新型生产要素的积累提供路径，还推动形成以脑疾病个性化诊疗为代表的新兴产业链，并在人机协同的脑机智能方向展现前瞻潜力。从技术自主化、产学研融合、伦理规范等维度提出了发展建议，旨在为中国抢占脑机接口战略制高点、塑造未来产业竞争力提供理论参考与分析框架。

, authors=陈小刚1, 陈菁菁2, 史楠林3, 高小榕3, *, authorsList=陈小刚, 陈菁菁, 史楠林, 高小榕, authorCompany=null, correspAuthors=高小榕, authorNote=

陈小刚，研究员，研究方向为脑机接口，电子信箱：

, correspAuthorsNote=
高小榕（通信作者），教授，研究方向为脑机接口，电子信箱：
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聚焦脑机接口创新发展，培育新质生产力
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陈小刚 1 , 陈菁菁 2 , 史楠林 3 , 高小榕 3, *
科技导报 | 政策建议 2026,44(18): 169-174
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科技导报 |政策建议 2026 , 44 (18) : 169 -174
聚焦脑机接口创新发展，培育新质生产力
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陈小刚1 , 陈菁菁2, 史楠林3, 高小榕3, *
作者信息
  • 1中国医学科学院 北京协和医学院生物医学工程研究所，天津 300192
  • 2清华大学教育学院，北京 100084
  • 3清华大学生物医学工程学院，北京 100084
通讯作者:
高小榕（通信作者），教授，研究方向为脑机接口，电子信箱：
作者简介:

陈小刚，研究员，研究方向为脑机接口，电子信箱：

Focus on the innovative development of brain−computer interface and cultivate new quality productivity
Xiaogang CHEN1 , Jingjing CHEN2, Nanlin SHI3, Xiaorong GAO3, *
Affiliations
  • 1Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300192, China
  • 2School of Education, Tsinghua University, Beijing 100084, China
  • 3School of Biomedical Engineering, Tsinghua University, Beijing 100084, China
出版时间: 2026-09-28 doi: 10.3981/j.issn.1000-7857.2025.07.00039
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摘要
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脑机接口作为融合脑科学、人工智能与工程技术的前沿方向，正成为培育新质生产力的重要载体。立足于脑机接口从科学研究走向产业落地的关键阶段，梳理了中国在政策布局、基础器件、编解码方法与典型应用系统等方面的最新进展，提出脑机接口不仅通过高效采集与处理脑数据为新型生产要素的积累提供路径，还推动形成以脑疾病个性化诊疗为代表的新兴产业链，并在人机协同的脑机智能方向展现前瞻潜力。从技术自主化、产学研融合、伦理规范等维度提出了发展建议，旨在为中国抢占脑机接口战略制高点、塑造未来产业竞争力提供理论参考与分析框架。

脑机接口  /  脑机智能  /  新质生产力

Brain−computer interface (BCI), as a cutting−edge field integrating brain science, artificial intelligence, and engineering technology, are becoming an important vehicle for cultivating new types of productivity. This study focuses on the crucial stage of BCI's transition from scientific research to industrial application, systematically reviewing our country's latest progress in policy layout, basic components, encoding and decoding methods, and typical application systems. It proposes that BCIs not only provide a path for the accumulation of new production factors through the efficient collection and processing of brain data, but also promote the formation of emerging industrial chains represented by personalized diagnosis and treatment of brain diseases, and demonstrate forward−looking potential in the field of human−machine collaborative brain−computer intelligence. Furthermore, this study proposes development suggestions from the dimensions of technological autonomy, industry−academia−research integration, and ethical standards, aiming to provide theoretical reference and analytical framework for our country to seize the strategic high ground in BCIs and shape its future industrial competitiveness.

brain−computer interface  /  brain−computer intelligence  /  new types of productivity
陈小刚, 陈菁菁, 史楠林, 高小榕. 聚焦脑机接口创新发展，培育新质生产力. 科技导报, 2026 , 44 (18) : 169 -174 . DOI: 10.3981/j.issn.1000-7857.2025.07.00039
Xiaogang CHEN, Jingjing CHEN, Nanlin SHI, Xiaorong GAO. Focus on the innovative development of brain−computer interface and cultivate new quality productivity[J]. Science & Technology Review, 2026 , 44 (18) : 169 -174 . DOI: 10.3981/j.issn.1000-7857.2025.07.00039
正文
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新质生产力已成为推动高质量发展的核心动力。发展新质生产力，必须前瞻布局未来产业，其中脑机接口（brain−computer interface，BCI）作为脑科学、信息技术与工程学交叉融合的前沿技术，是实现人机交互模式颠覆性变革的关键路径，也是新质生产力的典型代表。
自1973年概念提出以来，脑机接口历经50年发展，已进入从科学研究向产业落地转化的关键阶段[1−2]。该技术通过建立大脑与外部设备之间的直接通信通道，可替代、恢复或增强中枢神经系统的功能输出[3]。目前，脑机接口在医疗康复领域已取得显著突破，如帮助瘫痪患者实现站立、运动，协助“渐冻人”完成语言表达，以及通过意念驱动设备实现日常功能[4]。近年来，Neuralink、Synchron等企业推动技术持续演进，进一步拓展至交流辅助与意念控制设备等场景[5]。
随着技术成熟，脑机接口正逐步从医疗向军事、教育、娱乐等非医疗领域延伸，其战略价值日益凸显。2021年，美国商务部已将其列为影响国家安全的关键新兴技术；《Nature》亦于2024年将其评为7大值得关注技术之一[6]。全球多个主要国家和地区积极布局脑机接口产业，中国也加速推进该领域的技术创新与产业链协同，将其打造为培育新质生产力的重要阵地。
1 政策支持
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自2024年起，中国从国家到地方层面密集出台了一系列支持脑机接口技术与产业发展的政策，推动其从前沿科技加速走向战略型未来产业。2024年1月，中华人民共和国工业和信息化部（简称工信部）等7部门在《关于推动未来产业创新发展的实施意见》中，将脑机接口列为10大创新标志性产品，明确将制定专项政策。2024年12月，中共中央、国务院发布《关于深化养老服务改革发展的意见》中强调推动脑机接口等技术的研发与应用，为其在养老领域落地开辟路径。进入2025年，政策支持进一步体系化与具体化。国务院办公厅印发《关于全面深化药品医疗器械监管改革促进医药产业高质量发展的意见》，明确对脑机接口设备予以优先审评审批。国家药监局陆续推动相关行业标准立项与发布，包括术语规范与数据集质量要求，为产业标准化奠定基础。国家医保局首次设立脑机接口相关医疗服务价格项目，标志着该技术正式进入民生支付体系。2025年7月，工信部等7部门专门出台推动脑机接口产业创新发展的实施意见；同年10月，“十五五”规划建议更将脑机接口明确为未来产业6大方向之一，标志着其已上升至国家战略高度。地方层面积极响应，北京、上海、四川、山东等地于2025年相继发布产业行动方案，湖北更率先落地脑机接口医疗服务定价，加速技术临床应用进程。与此同时，伦理与标准体系同步构建。中华人民共和国科学技术部发布中国首部《脑机接口研究伦理指引》，防范科技伦理风险；中国信息通信研究院牵头制定《脑机接口标准化路线图》，引导产业规范发展。
2 脑机接口重要进展
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2.1 脑机接口基础器件
2.1.1 电极
电极作为与神经组织直接交互的核心部件，其性能直接影响信号质量与系统可靠性。非侵入式电极趋向高舒适性、隐蔽性与稳定性的方向发展。例如，清华大学团队研发的碳基MXene微针电极[7]具备良好的生物兼容性、高导电性与核磁兼容性，适用于智能驾驶、同步脑电−核磁等场景稳定采集（图1[7]）；宾夕法尼亚大学研究团队开发的毛发状电极[8]则以其轻巧、柔性及隐蔽特性，为长期无感监测提供了新思路。侵入式电极则聚焦于高密度、高生物相容性与长期稳定性。中国科学院脑智卓越中心及合作团队开发的超柔性血管内植入式电极uFINE−I[9]实现了大动物皮层单神经元级别的高分辨率记录；中国科学院上海微系统与信息技术研究所及合作团队研制的256通道μECoG电极阵列[10]则通过超薄网状结构紧密贴附皮层，实现了高保真信号采集并在拉布拉多犬上开展了200多天的在体实验。此外，美国团队推出的Neuropixels系列探针已将记录位点提升至5000个以上，支持多脑区大规模神经元同步记录，为神经环路研究提供了强大工具[11]。
2.1.2 神经信号采集系统
神经信号采集系统负责对电极捕获的微弱信号进行放大、滤波和数字化，其性能决定了整个系统的信噪比与稳定性。上海交通大学团队研发的直接转换模拟前端芯片实现了超过110 dB的动态范围[12]，显著提升了信号采集的精度与适应性。荷兰埃因霍芬理工大学研究团队则利用非晶铟镓锌氧化物薄膜晶体管技术，开发出柔性集成的模拟前端[13]，在噪声性能和能效上媲美硅基方案，推动了采集系统与电极的一体化与微型化。值得注意的是，Neuropixels 2.0系统将信号放大、调理与数字化电路高度集成于探头内部，实现了高通道数、全带宽并行采集，为大规模神经活动解析提供了硬件基础。
电极与神经信号采集系统需协同设计、共同演进。电极向高密度、高分辨率发展，对采集系统的处理、存储与传输能力提出更高要求；而采集系统则向集成化、低功耗与高通量方向推进，共同推动脑机接口前端性能的整体提升。
2.2 编解码方法
编解码方法是实现脑−机信息交互的核心环节，其发展正从依赖线性模型与先验知识，转向基于人工智能的非线性、自适应智能解码[14−15]。在非侵入式方面，英国格拉斯哥大学团队基于高密度频分复用的稳态视觉诱发电位编码[16]，大幅提升信息传输速率；中国医学科学院生物医学工程研究所团队则融合周围视野稳态视觉诱发电位与运动想象 [17]，通过混合编码提升视觉舒适性与任务执行效能（图2[17]）。在软件支撑层面，华南理工大学团队提出的BrainFusion框架实现了多模态神经信号的标准化集成与跨模态耦合分析，为复杂神经机制研究提供了统一平台[18]。侵入式编解码方面，浙江大学团队通过对高位截瘫患者书写动作神经活动的解析，揭示了精细动作的片段化编码机制[19]，并开发出基于形状与时间损失函数框架的解码算法[20]，实现了汉字书写轨迹的高精度还原。美国加州理工学院研究团队及合作者提出的基于卷积神经网络的特征提取方法FENet（feature extraction network）[21]，具备强泛化能力，可跨被试、跨脑区、跨任务直接应用，有效缓解了电极信号随时间退化的问题。此外，德克萨斯大学休斯顿健康科学中心研究团队与合作者利用迁移学习框架，实现了基于大规模立体定向脑电图（sEEG）群体数据的跨被试音素解码[22]，降低了对个体电极全覆盖的依赖，为通用型语音神经假体的开发奠定了基础。总体而言，编解码方法正朝着更自然、高效、鲁棒的方向演进，支撑脑机接口向实用化迈进。
2.3 应用场景
在应用场景方面，脑机接口技术已逐步拓展至运动控制、语言通信与神经康复等多个领域。在运动控制方面，清华大学与天津大学合作研发了基于忆阻器神经形态芯片的非侵入式脑机接口系统[23]，并成功实现了人脑对无人机的四自由度高效能操控。卡内基梅隆大学团队[24]与斯坦福大学团队[25]则分别在非侵入与侵入式层面实现对手指精细运动的实时解码与控制，为瘫痪患者的运动功能替代提供了可能。在语言解码方面，复旦大学附属华山医院与合作团队研发的256通道高通量柔性脑机接口系统（图3[26]），实现了对汉语音节的高精度识别与实时语句解码，速率接近日常对话需求[26]。在神经康复方面，美国德克萨斯大学奥斯汀分校的研究表明，经皮脊髓电刺激可通过调节运动皮层活动，加速脑机接口技能学习，为缩短康复训练周期提供了新途径[27]。
值得关注的是，脑机接口的临床试验与产业化进程明显加速。国内外多家机构相继开展植入式脑机接口的临床研究，例如Neuralink已完成多例患者植入，中国“北脑一号”脑机接口系统和博睿康[28]、脑虎科技、阶梯医疗等研发的系统也已进入临床试验阶段，初步在运动、感觉与交流功能恢复方面展现出积极效果[29−30]。与此同时，多地医院设立脑机接口中心或研究病房，标志着该技术正逐步走向规范化临床应用。
3 脑机接口面临的主要挑战
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20世纪90年代，中国研究团队就已经开展脑机接口相关研究。21世纪初至今，中国科研人员在面向脑状态识别算法开发的国际脑机接口比赛中多次斩获桂冠，并在脑机接口范式、算法等方面的研究中持续形成创新成果。30余年的耕耘为中国脑机接口相关产业的发展奠定了良好的基础，也为中国打造技术壁垒，发挥全球产业引领作用提供了机会。但值得注意的是，当前脑机接口研究成果大多处于实验室阶段，如何将学术界的前沿成果转化为可服务产业升级的新质生产力仍面临诸多技术创新与产业化方面的挑战。
1） 核心技术与关键器件依赖进口，制约自主发展。脑机接口的基础依赖于电极、芯片等硬件，用于大脑数据采集、传输和处理。中国信息通信研究院联合脑机接口产业联盟发布的《脑机接口技术发展与应用研究报告（2023年）》显示，全球脑机接口产业链中上游关键企业仅占8%，中国在高端电极、专用芯片等领域仍依赖国际供应，部分核心器件被列入出口管制清单，进而导致中国脑机接口研发与产业应用易受外部制约。缺乏自主知识产权的核心技术和器件，不仅阻碍了脑数据这一新型生产要素的高效积累，也限制了中国在全球脑机接口竞争中的话语权，成为产业安全发展的根本性瓶颈。
2） 技术链不完整与复合型人才严重短缺，产业化能力薄弱。脑机接口融合医学、电子、材料等多学科前沿技术，技术链条长且复杂，需要跨领域协同创新。市场上相关专业人才缺口大，尤其缺乏既懂神经科学又精通工程技术的复合型人才。这导致产业链中下游集成企业居多，上游基础创新不足，整体产业化进程缓慢。人才和技术链的断裂，使得中国脑机接口产品多停留在低端硬件和软件层面，难以实现从实验室到大规模应用的跨越。
3） 原始创新能力不足，制约其作为新质生产力的潜力释放。当前中国研究多集中在“跟随式”或“换场景式”创新层面，缺乏对技术根源和引领性架构的深度探索，难以支撑起以技术革命推动产业转型的战略需求。尤其在面对脑与精神疾病诊疗这类重大需求时，现有技术无法满足其对于干预靶点的精准性、系统长期运行的可靠性以及诊疗方案的真正个性化等严苛要求，一定程度上制约了脑机接口作为新质生产力在健康产业中的深度赋能。
4） 伦理规范与法律法规滞后，加之媒体报道失范，可能引发社会信任风险。脑机接口技术发展迅速，应用人群从患者拓展至更广泛人群，涉及脑数据隐私、知情同意等伦理挑战，但相关准则和法律法规尚未健全。同时，媒体对脑机接口缺乏专业认知，常进行夸大宣传或概念炒作，将无关成果包装为脑机接口突破，误导公众认知。这种过度炒作可能引发科技泡沫，消耗社会信心，影响产业长远可持续发展。
4 推进脑机接口健康发展的建议
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1） 集中资源攻关关键核心技术，构建自主可控的全产业链体系。应加大政策与资金扶持力度，支持学术界和产业界协同研发电极、芯片、算法等上游基础技术，积累自主知识产权，突破多模态、大规模神经元记录等前沿方向。结合超高速无线传输（5G及以上）和超算云平台，保障脑数据安全、高效采集与处理。通过上述努力，形成从器件、系统到应用的自主化产业链，降低对外依赖，确保脑数据作为重要资源的安全积累，为脑机接口前沿探索提供坚实支撑。
2） 打通技术链与补全人才链，深化产学研融合以提升产业化能力。引导高水平大学设立脑机接口交叉学科，建设人才培养基地，培育高质量复合型人才。鼓励国有企业与高校、科研院所共建创新平台，承接脑机接口重点攻关任务，打造产学研一体化模式。推动全产业链集群发展，解决从技术研发到产品落地的难题，逐步建立健康、健全的脑机接口产业链，提高系统级产品的研发与制造能力。
3） 强化原创创新与重大应用转化。中国在非侵入式技术方面已具备一定积累，应避免陷入低水平重复研究，聚焦适应性人机协同学习、动态神经解码与调控等原创性、颠覆性技术路径。围绕国家重大需求，如脑疾病诊疗、下一代消费电子交互等，组织产学研联合攻关，推动技术在高标准、严要求的实际场景中落地验证，加速实验室技术向工业化、产品化转化，构建自主可控的技术链与产业链，从而引领新一代脑机接口技术的发展方向。
4） 加快建立伦理准则并规范媒体报道，引导产业健康有序发展。组织多方利益相关者共同制定可操作的脑机接口伦理指南，明确数据隐私、安全使用和知情同意等规范，推动相关法律法规出台。同时，加强科学普及，建立权威信息发布窗口，客观传递技术进展。通过政策引导规范媒体报道，防止片面炒作和概念化宣传，营造理性社会认知环境，规避科技泡沫风险，保障脑机接口在伦理与舆论双重维度上稳健前行。
5 结论
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脑机接口正处于从实验室突破迈向产业转化的关键拐点，其通过高效采集与解析脑数据为新型生产要素的积累提供了路径，催生了以脑疾病精准诊疗为代表的新兴产业链，并在人机协同的脑机智能范式上展现出巨大潜力。面对这一培育新质生产力的战略机遇，我们必须聚焦底层器件自主化、产学研用深度融合与伦理规范前瞻构建，方能牢牢掌握未来智能时代的发展主动权，使脑机接口真正成为推动社会进步与产业变革的引领性力量。
基金
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  • 国家自然科学基金项目(62471495)
  • 国家自然科学基金项目(62376035)
  • 天津市科技计划项目(24JCJQJC00040)
  • 天津市科技计划项目(24JCZDJC00430)
  • 中国医学科学院医学与健康科技创新工程项目(2025−I2M−TS−10)
文献
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2026年第44卷第18期
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doi: 10.3981/j.issn.1000-7857.2025.07.00039
  • 接收时间：2025-07-07
  • 首发时间：2026-11-03
  • 出版时间：2026-09-28
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  • 收稿日期：2025-07-07
  • 修回日期：2026-01-13
基金
国家自然科学基金项目(62471495)
国家自然科学基金项目(62376035)
天津市科技计划项目(24JCJQJC00040)
天津市科技计划项目(24JCZDJC00430)
中国医学科学院医学与健康科技创新工程项目(2025−I2M−TS−10)
作者信息
    1中国医学科学院 北京协和医学院生物医学工程研究所，天津 300192
    2清华大学教育学院，北京 100084
    3清华大学生物医学工程学院，北京 100084

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高小榕（通信作者），教授，研究方向为脑机接口，电子信箱：
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