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基于 SSVEP-BCI 的在线辅助设备控制六自由度机械臂。

Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.

机构信息

Department of Energy and Mechanics Sciences, Universidad de las Fuerzas Armadas, Sangolqui 171103, Ecuador.

Department of Electrical, Electronics and Telecommunications Engineering, Universidad de las Fuerzas Armadas, Sangolqui 171103, Ecuador.

出版信息

Sensors (Basel). 2024 Mar 17;24(6):1922. doi: 10.3390/s24061922.

DOI:10.3390/s24061922
PMID:38544185
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10974467/
Abstract

This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.

摘要

本文探讨了将利用视觉诱发电位范式（SSVEP）的脑机接口（BCI）与六自由度（6-DOF）机械臂集成，以增强康复工具的潜在益处。SSVEP-BCI 使用脑电图（EEG）作为一种测量枕叶中神经反应的方法，以应对预先建立的视觉刺激频率。BCI 的离线和在线研究分别产生了 75%和 83%的准确率，表明该系统在准确检测和捕捉用户意图方面的有效性。机械臂通过使用总共五个控制频率来实现平面运动。实验结果表现出高精度和一致性，准确性和重复性的记录值分别为±0.85 和±1.49 cm。此外，在每个平面内构建正方形的任务性能测试中，系统的准确率达到了 79%和 83%。SSVEP-BCI 和机械臂的结合使用显示出了很大的潜力，为开发旨在改善肌萎缩侧索硬化症、脊柱裂和其他相关疾病患者健康的辅助技术奠定了坚实的基础。

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本文引用的文献

1
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Arab J Sci Eng. 2023 Mar 25:1-16. doi: 10.1007/s13369-023-07720-0.
2
An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey.基于稳态视觉诱发电位的脑机接口中深度学习模型分析：一项综述
Brain Sci. 2023 Mar 13;13(3):483. doi: 10.3390/brainsci13030483.
3
Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control.
基于快速响应码模式的稳态视觉诱发电位的脑-机接口用于轮椅控制。
Sensors (Basel). 2023 Feb 12;23(4):2069. doi: 10.3390/s23042069.
4
Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm.基于 SSVEP 的脑机接口在 6 自由度机器人手臂控制中的跨平台实现。
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5
SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation.基于 SSVEP 的脑机接口控制的用于脑卒中后手部功能康复的软体机器人手套。
IEEE Trans Neural Syst Rehabil Eng. 2022;30:1737-1744. doi: 10.1109/TNSRE.2022.3185262. Epub 2022 Jul 4.
6
Enhanced System Robustness of Asynchronous BCI in Augmented Reality Using Steady-State Motion Visual Evoked Potential.利用稳态运动视觉诱发电位增强增强现实异步脑机接口的系统鲁棒性。
IEEE Trans Neural Syst Rehabil Eng. 2022;30:85-95. doi: 10.1109/TNSRE.2022.3140772. Epub 2022 Jan 28.
7
Effect of BCI-Controlled Pedaling Training System With Multiple Modalities of Feedback on Motor and Cognitive Function Rehabilitation of Early Subacute Stroke Patients.多模态反馈脑机接口控制蹬踏训练系统对早期亚急性期脑卒中患者运动和认知功能康复的影响。
IEEE Trans Neural Syst Rehabil Eng. 2021;29:2569-2577. doi: 10.1109/TNSRE.2021.3132944. Epub 2021 Dec 21.
8
Improving the Performance of Individually Calibrated SSVEP-BCI by Task- Discriminant Component Analysis.通过任务判别成分分析提高个体校准 SSVEP-BCI 的性能。
IEEE Trans Neural Syst Rehabil Eng. 2021;29:1998-2007. doi: 10.1109/TNSRE.2021.3114340. Epub 2021 Oct 1.
9
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Sensors (Basel). 2021 Aug 6;21(16):5308. doi: 10.3390/s21165308.
10
Brain-Computer Interface Speller Based on Steady-State Visual Evoked Potential: A Review Focusing on the Stimulus Paradigm and Performance.基于稳态视觉诱发电位的脑机接口拼写器：聚焦刺激范式与性能的综述
Brain Sci. 2021 Apr 1;11(4):450. doi: 10.3390/brainsci11040450.