自适应脑机接口研究综述

肖晓琳; 辛风然; 梅杰; 李昂; 曹洪涛; 徐舫舟; 许敏鹏; 明东

doi:10.11999/JEIT220707

自适应脑机接口研究综述

doi: 10.11999/JEIT220707

肖晓琳^{1, 2},
辛风然¹,
梅杰²,
李昂²,
曹洪涛¹,
徐舫舟³,
许敏鹏^{1, 2, ,},
明东^{1, 2}

1.
天津大学医学工程与转化医学研究院天津 300072
2.
天津大学精密仪器与光电子工程学院天津 300072
3.
齐鲁工业大学电子信息工程学院济南 250306

基金项目: 国家自然科学基金(62106170, 81925020, 62122059, 61976152, 62106173)，济南市“新高校20条”引进创新团队项目(2021GXRC071)

详细信息

作者简介:
肖晓琳：女，讲师，研究方向为脑-机接口、脑电信号处理、脑控系统设计开发

辛风然：女，硕士生，研究方向为脑-机接口

梅杰：男，博士生，研究方向为脑-机接口

李昂：女，博士生，研究方向为脑-机接口

曹洪涛：男，硕士生，研究方向为脑-机接口

徐舫舟：女，讲师，研究方向为脑机融合通信与控制、神经与康复工程

许敏鹏：男，副教授，研究方向为脑-机接口、神经信号处理和神经调控

明东：男，教授，研究方向为脑-机接口、神经再生与修复、神经放生与智能、神经刺激与调节、神经传感与成像

通讯作者:
许敏鹏　xmp52637@tju.edu.cn

中图分类号: TN911.7; TP391
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出版历程
- 收稿日期: 2022-05-31
- 修回日期: 2022-08-31
- 网络出版日期: 2022-09-02
- 刊出日期: 2023-07-10

A Review of Adaptive Brain-Computer Interface Research

XIAO Xiaolin^{1, 2},
XIN Fengran¹,
MEI Jie²,
LI Ang²,
CAO Hongtao¹,
XU Fangzhou³,
XU Minpeng^{1, 2
, ,},
MING Dong^{1, 2}

1.
Academy of Engineering and Translational Medicine, Tianjin University, Tianjin 300072, China
2.
School of Precision Instrument and Opto-electronics Engineering, Tianjin University, Tianjin 300072, China
3.
School of Electronic and Information Engineering, Qilu University of Technology, Jinan 250306, China

Funds: The National Natural Science Foundation of China (62106170, 81925020, 62122059, 61976152, 62106173), the Introduction of Innovative Team Projects in Jinan “20 New Universities” (2021GXRC071)

摘要

摘要: 脑机接口(BCI)不依赖于外周神经和肌肉，在大脑与外部设备之间建立起直接交流的通路。近年来，该技术在识别准确率和系统交互速率方面已取得巨大突破。然而，脑电(EEG)信号非平稳特性较强且用户主观状态波动较大，传统脑机接口技术对大脑活动的动态变化欠缺适应性，影响了脑机接口系统的控制稳定性，也限制了其智能化发展和应用。自适应脑机接口可根据大脑当前状态动态调整诱发范式和实时更新识别模型，从而增强脑控系统对非平稳大脑活动的适应性，提高其控制精度和鲁棒性，实现更加实用化的脑控系统，对推动脑机接口技术进一步发展极具意义。该文对自适应脑机接口的相关研究进行了回顾和总结，并对该技术未来发展的方向进行了展望。
- 脑机接口 /
- 脑电 /
- 自适应脑机接口
Abstract: Brain-Computer Interface(BCI) establishes a direct communication pathway between the brain and external devices without relying on peripheral nerves and muscles. In recent years, great breakthroughs in recognition accuracy and system interaction rate have been made by this technology. However, the non-stationary characteristics of ElectroEncephaloGram(EEG) signals are strong and the user's subjective state fluctuates greatly. Traditional BCI technology lacks adaptability to the dynamic changes of brain activity, so the control stability of the BCI system is affected and its intelligence development and application are limited. The adaptive BCI can dynamically adjust the evoked paradigm and update the recognition model in real time according to the current state of the brain, thereby enhancing the adaptability of the brain control system to non-stationary brain activities, improving its control accuracy and robustness, and achieving a more practical brain control system, which is highly meaningful to push the further development of BCI technology. The related research of adaptive BCI is reviewed and summarized in this paper, and an outlook of the future development direction of this technology is given.
- Brain-Computer Interface(BCI) /
- ElectroEncephaloGram(EEG) /
- Adaptive Brain-Computer Interface(BCI)

HTML全文

图 1 传统脑机接口框架

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图 2 自适应脑机接口框架

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表 1 传统脑机接口与自适应脑机接口对比

脑机接口类型	传统脑机接口	自适应脑机接口
诱发范式	诱发参数固定	诱发时长或刺激序列等参数可根据用户大脑状态动态调整
识别模型	模型参数固定	特征提取模型或模式识别模型可针对EEG高时变特点实时更新
小结	(1) 从诱发范式角度看，自适应脑机接口可最大化诱发特征的有效性，提升人机交互效率； (2) 从识别模型角度看，自适应脑机接口可提高EEG特征的利用率，提升系统的稳定性和鲁棒性； (3) 从实际应用角度看，两种类型的脑机接口应用场景相似，但自适应脑机接口由于其高适应性、高鲁棒性的特点，优势更为突出。

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