非侵入式连续运动控制脑-机接口技术综述

许敏鹏; 贾乐怡; 周晓宇; 陈恩泽; 王俊洋; 肖晓琳; 明东

doi:10.11999/JEIT260011

非侵入式连续运动控制脑-机接口技术综述

doi: 10.11999/JEIT260011 cstr: 32379.14.JEIT260011

许敏鹏^{1, 2},
贾乐怡²,
周晓宇²,
陈恩泽²,
王俊洋²,
肖晓琳^{1, 2, ,},
明东^{1, 2}

1.
天津大学医学院天津 300072
2.
天津大学医学工程与转化医学研究院天津 300072

基金项目: 国家自然科学基金(82330064)

详细信息

作者简介:
许敏鹏：男，教授，研究方向为脑机接口

贾乐怡：女，硕士生，研究方向为脑机接口

周晓宇：女，博士生，研究方向为脑机接口

陈恩泽：男，博士生，研究方向为脑机接口

王俊洋：男，博士生，研究方向为脑机接口

肖晓琳：女，副教授，研究方向为视觉型脑机接口、高速高维脑操控系统、视觉脑控游戏、临床视功能检测

明东：男，教授，研究方向为脑机接口

通讯作者:
肖晓琳　Email：xiaoxiao0@tju.edu.cn

中图分类号: TP11; R318.0
计量
- 文章访问数: 11
- HTML全文浏览量: 2
- PDF下载量: 0
- 被引次数: 0
出版历程
- 修回日期: 2026-03-16
- 录用日期: 2026-03-16
- 网络出版日期: 2026-04-06

Review of Non-invasive Brain–Computer Interfaces for Continuous Motor Control

XU Minpeng^{1, 2},
JIA Leyi²,
ZHOU Xiaoyu²,
CHEN Enze²,
WANG Junyang²,
XIAO Xiaolin^{1, 2
, ,},
MING Dong^{1, 2}

1.
Medical school, Tianjin University, Tianjin 300072, China
2.
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China

Funds: The National Natural Science Foundation of China (82330064)

摘要

摘要: 脑–机接口(BCI)在外部设备运动控制中的应用不断拓展，但以离散指令为主的控制方式难以满足连续控制与自然交互的需求，非侵入式连续运动控制BCI因兼具安全性与可推广性而受到广泛关注。本文系统综述了非侵入式连续运动控制BCI的研究进展，从控制范式、解码方法、应用场景及评价指标等方面进行了综合分析，重点讨论了连续控制实现机制及其对系统性能的影响。综述结果表明，非侵入式连续运动控制BCI已由早期概念验证任务逐步拓展至多类实际应用场景，但在范式优化、解码性能提升、真实场景适配和评价标准完善等方面仍有待进一步发展。本文在此基础上对相关研究进行了归纳总结，并对未来发展方向进行了展望，为非侵入式连续运动控制BCI的进一步研究与应用提供参考。
- 脑-机接口 /
- 脑电 /
- 连续控制脑-机接口
Abstract: Significance Continuous motor control is a fundamental capability for brain–computer interface (BCI) systems aiming at natural and efficient interaction with external devices. Compared with discrete command-based control, continuous control enables real-time and smooth regulation of motion parameters such as position, velocity, and trajectory, which is essential for applications including assistive mobility, neuro rehabilitation, robotic manipulation, and immersive human–machine interaction. Although invasive BCI s have demonstrated high-performance continuous control benefiting from high-quality neural recordings, their reliance on surgical implantation restricts long-term use and large-scale deployment. Therefore, a systematic review of non-invasive continuous motor control BCI technologies is necessary to clarify research progress, methodological characteristics, and remaining challenges. Progress This review summarizes advances in non-invasive continuous motor control BCIs from four closely related aspects: control paradigms, decoding algorithms, application scenarios, and performance evaluation. At the paradigm level, motor imagery, steady-state visual evoked potentials, event-related potentials, and hybrid paradigms have been investigated to support continuous control through sustained intention modulation, dynamic stimulus encoding, and hierarchical or shared-control strategies. Regarding decoding algorithms, two major frameworks are identified: motion parameter mapping methods and motion parameter regression methods. Motion parameter mapping methods achieve continuous output by temporally integrating discrete classification results or mapping them to velocity or state variables, whereas motion parameter regression methods directly establish relationships between EEG features and continuous kinematic parameters. In recent studies, nonlinear models and deep learning approaches have been increasingly incorporated to improve robustness under non-stationary EEG conditions. At the application level, non-invasive continuous control has evolved from two-dimensional cursor tasks to more practical scenarios such as wheelchair navigation, robotic arm manipulation, unmanned systems, and virtual or augmented reality environments. In addition, existing studies evaluate continuous control performance using both objective metrics (e.g., trajectory error, task success rate, and information transfer rate) and subjective measures (e.g., workload and user experience), reflecting diverse experimental designs and control objectives. Conclusions Overall, existing studies demonstrate that non-invasive BCIs are capable of supporting continuous motor control; however, current research remains at a stage where diverse methods coexist without a unified framework. At the paradigm level, different approaches vary in their ability to reliably elicit and sustain continuous motor intentions. In terms of decoding algorithms, both motion parameter mapping and regression methods face limitations in robustness, generalization, and long-term stability due to the non-stationary nature of EEG signals. At the application level, many studies are still constrained to specific tasks and controlled environments, and the transferability of continuous control strategies to complex real-world scenarios requires further validation. Moreover, the lack of standardized evaluation protocols hinders direct comparison and systematic optimization across studies. Prospects Future research should focus on improving the stability and reliability of continuous control paradigms, enhancing decoding robustness under realistic EEG conditions, and strengthening the alignment between control strategies and application requirements. Establishing unified evaluation frameworks that integrate both objective and subjective indicators will be critical for methodological convergence and fair comparison. With continued advances, non-invasive continuous motor control BCIs are expected to play an increasingly important role in assistive technologies, rehabilitation systems, and advanced human–machine interaction.
- Brain–Computer Interface (BCI) /
- Electroencephalogram(EEG) /
- Continuous Motor Control Brain–Computer Interface (BCI)

HTML全文

图 1 非侵入式连续控制流程图

下载: 全尺寸图片幻灯片

表 1 连续控制任务中常用的评价指标

指标类型	具体指标
客观指标	成功率^[42,62–67]、完成时间^{[28,62,65,66]}、轨迹有效率^[28,62–65]、Fitts’ITR^[28]、位置误差^{[28,62,66,68]}
主观指标	NASA量表：心理需求、身体需求、时间需求、绩效水平、努力程度、挫败感^[60,62]
	QCM量表：注意力水平、认知困难、精神负担、心理努力、疲惫感、不轻松感、负担感^[59]
	自定义问卷：厌烦感、疲劳感、困难程度、喜好、不舒服感、头晕感^{[28,59,65,69,70]}

下载: 导出CSV

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