非侵入式脑机接口在机器人控制领域的研究综述

doi:10.3901/JME.2025.15.105

机械工程学报 ›› 2025, Vol. 61 ›› Issue (15): 105-120.doi: 10.3901/JME.2025.15.105

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非侵入式脑机接口在机器人控制领域的研究综述

高晗¹, 蒲琪然¹, 赵永生¹, 张茂林², 吴紫涧¹, 程宝平¹, 王柏村²

1. 中移(杭州)信息技术有限公司杭州 311100;
2. 浙江大学机械工程学院杭州 310058

收稿日期:2024-11-25 修回日期:2025-01-14 发布日期:2025-09-28
作者简介:高晗，女，1997年出生，博士。主要研究方向为人工智能、脑机接口及其在机器人领域的应用。E-mail：gaohan@cmhi.chinamobile.com;蒲琪然，女，1989年出生，硕士。主要研究方向为智能机器人系统、人机交互、脑机接口及其在机器人领域的应用。E-mail：puqiran@cmhi.chinamobile.com;赵永生，男，1989年出生，博士。主要研究方向为智能机器人环境感知、手眼协作、视觉伺服等关键技术。E-mail：zhaoyongsheng@cmhi.chinamobile.com;张茂林，男，2001年出生，博士研究生。主要研究方向为脑机接口、人机协作。E-mail：maolin_zhang@zju.edu.cn;吴紫涧，女，1989年出生，博士。主要研究方向为智能制造、工业机器人。E-mail：wuzijian@cmhi.chinamobile.com;程宝平(通信作者)，男，1980年出生，博士。主要研究方向为多媒体通信，智能机器人系统，脑机接口。E-mail：chengbaoping@cmhi.chinamobile.com;王柏村，男，1990年出生，博士。主要研究方向为智能制造，人机协作，脑机接口。E-mail：baicunw@zju.edu.cn
基金资助:
国家自然科学基金(62171257);国家自然科学基金-企业联合基金(U22B2001)资助项目。

A Review of Non-invasive Brain-computer Interface Research in Robotic Control

GAO Han¹, PU Qiran¹, ZHAO Yongsheng¹, ZHANG Maolin², WU Zijian¹, CHENG Baoping¹, WANG Baicun²

1. China Mobile (Hangzhou) Information Technology Company, Ltd., Hangzhou 311100;
2. School of Mechanical Engineering, Zhejiang University, Hangzhou 310058

Received:2024-11-25 Revised:2025-01-14 Published:2025-09-28

摘要/Abstract

摘要： 非侵入式脑机接口(Brain-computer interface, BCI)技术作为一种新兴的人机交互方式，在机器人控制领域展现了广阔的应用前景。首先概述其发展背景与重要性，并深入探讨脑电信号的生理基础，阐明脑电图(Electroencephalography, EEG)以其无创性和便捷性成为BCI系统的常用测量手段。随后，分析了典型BCI范式的优劣特点和适用场景——包括主动式如运动想象、反应式如稳态视觉诱发电位(Steady-state visual evoked potential, SSVEP)、事件相关电位P300，以及结合多种范式优势的混合范式，展示了这些范式如何实现复杂且高效的机器人控制任务。此外，系统地介绍了EEG信号采集、预处理及模式识别的关键步骤，强调了深度学习在提高解码精度方面的作用，同时也指出了其面临的挑战，如数据量需求大和模型解释性差。最后，总结了BCI技术的发展趋势和研究挑战，提出了推动非侵入式BCI技术在实际机器人控制应用中进一步发展的方向。综上所述，不仅对非侵入式BCI技术在机器人控制领域应用进行了探讨，还强调了该技术在未来可能带来的变革性影响，为后续研究提供参考和启发。

关键词: 脑机接口, 机器人控制, 运动想象, 稳态视觉诱发电位, 模式识别

Abstract: Non-invasive brain-computer interface（BCI） technology, as an emerging human-computer interaction method, has demonstrated broad application prospects in the field of robot control. This study firstly outlines the background and importance of its development, and deeply discusses the physiological basis of brain electrical activity, clarifying how electroencephalography（EEG） has become a common measurement tool for BCI systems due to its non-invasiveness and convenience. Subsequently, this study analyzes the advantages and disadvantages of typical EEG paradigms and applicable scenarios-including active ones such as motor imagery, reactive ones such as steady-state visual evoked potential（SSVEP）, event-related potential P300, and hybrid paradigms that combine the advantages of multiple paradigms. hybrid paradigms that combine the advantages of multiple paradigms, showing how these paradigms can realize complex and efficient robot control tasks. In addition, this study systematically introduces the key steps from EEG signal acquisition to preprocessing and pattern recognition, emphasizes the role of deep learning in improving decoding accuracy, and also points out its challenges, such as high data volume requirements and poor model interpretability. Finally, this study summarizes the development trends and research challenges of BCI technology, and proposes directions to promote the further development of non-invasive BCI technology in practical robot control applications. In summary, this study not only provides an exploration of the application of non-invasive BCI technology in robot control, but also emphasizes the transformative impact that this technology may bring in the future, providing reference and inspiration for subsequent research.

Key words: brain-computer interface, robotic control, motor imagery, steady-state visual evoked potential, pattern recognition

中图分类号:

TP391

高晗, 蒲琪然, 赵永生, 张茂林, 吴紫涧, 程宝平, 王柏村. 非侵入式脑机接口在机器人控制领域的研究综述[J]. 机械工程学报, 2025, 61(15): 105-120.

GAO Han, PU Qiran, ZHAO Yongsheng, ZHANG Maolin, WU Zijian, CHENG Baoping, WANG Baicun. A Review of Non-invasive Brain-computer Interface Research in Robotic Control[J]. Journal of Mechanical Engineering, 2025, 61(15): 105-120.

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非侵入式脑机接口在机器人控制领域的研究综述

A Review of Non-invasive Brain-computer Interface Research in Robotic Control

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