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一种基于运动想象与眼动追踪相结合的混合脑机接口的高性能通用计算机光标控制方案。

A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.

作者信息

Zhang Jiakai, Zhang Yuqi, Zhang Xinlong, Xu Boyang, Zhao Huanqing, Sun Tinghui, Wang Ju, Lu Shaojie, Shen Xiaoyan

机构信息

School of Information Science and Technology, Nantong University, Nantong 226019, China.

Nantong Research Institute for Advanced Communication Technologies, Nantong University, Nantong 226019, China.

出版信息

iScience. 2024 May 31;27(6):110164. doi: 10.1016/j.isci.2024.110164. eCollection 2024 Jun 21.

DOI:10.1016/j.isci.2024.110164
PMID:38974471
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11225862/
Abstract

This study introduces a novel virtual cursor control system designed to empower individuals with neuromuscular disabilities in the digital world. By combining eye-tracking with motor imagery (MI) in a hybrid brain-computer interface (BCI), the system enhances cursor control accuracy and simplicity. Real-time classification accuracy reaches 87.92% (peak of 93.33%), with cursor stability in the gazing state at 96.1%. Integrated into common operating systems, it enables tasks like text entry, online chatting, email, web surfing, and picture dragging, with an average text input rate of 53.2 characters per minute (CPM). This technology facilitates fundamental computing tasks for patients, fostering their integration into the online community and paving the way for future developments in BCI systems.

摘要

本研究介绍了一种新型虚拟光标控制系统，旨在使患有神经肌肉疾病的个体在数字世界中获得能力。通过在混合脑机接口（BCI）中将眼动追踪与运动想象（MI）相结合，该系统提高了光标控制的准确性和简易性。实时分类准确率达到87.92%（峰值为93.33%），注视状态下光标的稳定性为96.1%。该系统集成到常见操作系统中，可实现文本输入、在线聊天、电子邮件、网页浏览和图片拖动等任务，平均文本输入速率为每分钟53.2个字符（CPM）。这项技术为患者完成基本的计算任务提供了便利，促进了他们融入在线社区，并为BCI系统的未来发展铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/6f3c5c9c073f/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/03ea740746e5/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/034161564a61/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/c2ef6cdab280/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/7bb5ff1acb4d/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/6bc48a0d244a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/6f3c5c9c073f/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/03ea740746e5/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/034161564a61/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/c2ef6cdab280/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/7bb5ff1acb4d/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/6bc48a0d244a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/415e/11225862/6f3c5c9c073f/gr5.jpg

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2
Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals.用于解码跨会话运动想象脑电图信号的黎曼几何和集成学习。
J Neural Eng. 2023 Nov 22;20(6). doi: 10.1088/1741-2552/ad0a01.
3
Cross-dataset transfer learning for motor imagery signal classification via multi-task learning and pre-training.
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J Neural Eng. 2023 Oct 20;20(5). doi: 10.1088/1741-2552/acfe9c.
4
MI-based BCI with accurate real-time three-class classification processing and light control application.基于 MI 的脑机接口，具有精确的实时三分类处理和光控应用。
Proc Inst Mech Eng H. 2023 Aug;237(8):1017-1028. doi: 10.1177/09544119231187287. Epub 2023 Aug 7.
5
Brain-Controlled, AR-Based Home Automation System Using SSVEP-Based Brain-Computer Interface and EOG-Based Eye Tracker: A Feasibility Study for the Elderly End User.基于稳态视觉诱发电位的脑机接口和基于眼电图的眼动追踪器的脑控增强现实家庭自动化系统：针对老年终端用户的可行性研究
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6
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7
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