基于脑电的快速序列视觉呈现脑-机接口系统研究进展综述

魏玮; 邱爽; 李叙锦; 毛嘉宇; 王妍紫; 何晖光

doi:10.11999/JEIT230952

基于脑电的快速序列视觉呈现脑-机接口系统研究进展综述

doi: 10.11999/JEIT230952

魏玮¹,
邱爽^{1, 2},
李叙锦^{1, 2},
毛嘉宇^{1, 2},
王妍紫^{1, 2},
何晖光^{1, 2, ,}

1.
中国科学院自动化研究所多模态人工智能系统全国重点实验室脑图谱与类脑智能实验室北京 100190
2.
中国科学院大学北京 100049

基金项目: 国家自然科学基金(62206285, U21A20388, 62020106015)，中国博士后科学基金(2021M703490)

详细信息

作者简介:
魏玮：男，助理研究员，研究方向为脑-机接口、模式识别

邱爽：女，副研究员，研究方向为脑-机接口、人机交互

李叙锦：男，博士生，研究方向为脑-机接口、深度学习

毛嘉宇：男，博士生，研究方向为脑-机接口、多模态机器学习

王妍紫：女，硕士生，研究方向为脑-机接口、认知负荷

何晖光：男，研究员，研究方向为脑-机接口、模式识别与人工智能、医学影像处理

通讯作者:
何晖光　huiguang.he@ia.ac.cn

中图分类号: TN911.7; TP391
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出版历程
- 收稿日期: 2023-03-10
- 修回日期: 2023-11-30
- 网络出版日期: 2023-12-06
- 刊出日期: 2024-02-29

A Review of Research Progress on Brain-Computer Interface Systems for Rapid Serial Visual Presentation Based on ElectroEncephaloGram

WEI Wei¹,
QIU Shuang^{1, 2},
LI Xujin^{1, 2},
MAO Jiayu^{1, 2},
WANG Yanzi^{1, 2},
HE Huiguang^{1, 2
, ,}

1.
Laboratory of Brain Atlas and Brain-inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: The National Natural Science Foundation of China (62206285, U21A20388, 62020106015), General Program of China Postdoctoral Science Foundation (2021M703490)

摘要

摘要: 脑-机接口(BCI)系统建立大脑与外部设备之间的直接交流通路，结合快速序列视觉呈现(RSVP)范式能够实现利用人类视觉系统进行高流通量图像目标检索。近些年来，RSVP-BCI系统在范式编码、脑电(EEG)解码和系统应用方面的研究取得了长足的进步。对范式编码的研究揭示不同范式参数对系统性能的影响，促进提升系统性能；脑电解码的研究在提升算法分类性能的同时推动少训练、零训练样本、多模态等场景下的应用；对RSVP-BCI系统应用的研究实现推动系统走向实际应用并拓宽了应用领域。同时，系统仍面临着迈向实际时可应用领域范围窄、脑电跨域解码难题以及计算机视觉飞速进步带来的挑战。该文对RSVP-BCI近年来的相关研究进展进行了回顾与总结，并对未来的发展方向进行了展望。
- 脑-机接口 /
- 快速序列视觉呈现 /
- 脑电
Abstract: Brain-Computer Interface (BCI) system establishes a direct communication pathway between the brain and external devices, and combined with the Rapid Serial Visual Presentation (RSVP) paradigm, it can achieve high-throughput target image retrieval by utilizing the human visual system. In recent years, the RSVP-BCI system has made significant progress in research on paradigm, ElectroEncephaloGram (EEG) decoding, and system applications. Research on paradigm reveals the impact of different paradigm parameters on system performance, promoting the improvement of system performance; The research on EEG decoding improves the classification performance of algorithms and promotes applications in scenarios such as few training, zero training samples, and multimodality; The research on the RSVP-BCI system application has driven the system towards practical applications and expanded its application fields. However, the system also faces challenges such as limited practical applications, difficulties in cross-domain decoding of EEG, and the rapid progress of computer vision. This article reviews and summarizes the research progress of RSVP-BCI in recent years, and looks forward to the future development direction.
- Brain-Computer Interface (BCI) /
- Rapid Serial Visual Presentation (RSVP) /
- ElectroEncephaloGram (EEG)

HTML全文

图 1 RSVP-BCI系统示意图

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图 2 基于RSVP的目标检索任务中多名被试平均的ERP波形

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图 3 双视觉通路RSVP范式示意图(引自文献 [6])

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图 4 实现从源域到目标域的知识迁移存在两种方法^[28]

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图 5 RSVP-BCI系统部分应用示意图

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表 1 RSVP编码研究进展简表

文献	时间	范式因素	应用	研究内容和主要结论
[5]	2019年	呈现速率	目标检索	研究呈现速率与认知负荷之间的关系，结果显示随着呈现速率的增加，人员的认知负荷增加，任务性能降低。
[6]	2015年	呈现模式	目标检索	研究了一种左右排列的双视觉通路RSVP，实现优于单视觉通路RSVP的性能。
[7,8]	2018/2017年	呈现模式	目标检索	研究了单/双三视觉通路RSVP，分别实现0.926,0.946和0.952的分类AUC。
[9]	2022年	呈现模式	目标检索	研究了双/三视觉通路范式下视野对RSVP目标检索的影响，研究结果表明视野对目标检索性能有显著影响，中心视野优于周边视野，左视野高于右视野，上视野优于下视野。
[11]	2017年	呈现模式	拼写器	研究了字符随机方向运动的RSVP字符拼写器范式，实现诱发更强的P300信号和拼写器字符识别准确率提升。
[12]	2019年	呈现模式	拼写器	研究了双/三视觉通路RSVP拼写器，实现提高系统ITR，其中三视觉通路最高实现了在线平均ITR为20.26 bpm。
[13]	2020年	混合范式	拼写器	研究了RSVP拼写器中的第1个混合范式——RSVP-SSVEP BCI，实现平均信息传输率达到23.41 bpm。

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表 2 零校准脑电解码性能(%)

文献	方法名称	时间	均衡精度
[36]	STIG	2016年	65.82 ± 7.76
[37]	–	2020年	85.03 ± 4.70
[38]	EPMN	2022年	86.34 ± 3.54
[39]	–	2022年	86.76 ± 3.93
[40]	TFF-Former	2022年	88.05 ± 3.73

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表 3 多模态脑电解码性能对比

文献	时间	任务类型	模态	单模态性能	多模态性能
[41]	2016年	图像目标检索	1. 脑电 2. 图像	[脑电] BA: ～74% [图像] BA: ～81%	BA：85.06%
[25]	2023年	图像目标检索	1. 脑电 2. 眼动	[脑电] BA: 81.85% [眼动] BA: 66.97%	BA: 88.00%
[42]	2018年	身份认证	1. 脑电 2. 眼电	[脑电] Accuracy: 92.40%	Accuracy: 97.60%
[43]	2019年	目标检索	1. 脑电 2. 眼电 3. 肌电	[脑电] F1-score: 0.602	F1-score: 0.657

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