A comprehensive study of EEG-based control of artificial arms

Author:

Satam IhabORCID

Abstract

Introduction/purpose: The electroencephalography (EEG) signal has a great impact on the development of prosthetic arm control technology. EEG signals are used as the main tool in functional investigations of human motion. The study of controlling prosthetic arms using brain signals is still in its early stages. Brain wave-controlled prosthetic arms have attracted researchers' attention in the last few years. Methods: Several studies have been carried out to systematically review published articles as a means of offering researchers and experts a comprehensive summary of the present, state-of-the-art EEG-based control techniques used in the prosthetic arm and other technologies. Results: 175 articles were studied, compared, and filtered to only include the articles that have strong connections to the study. Conclusion: This study has three goals. The first one is to gather, summarize, and evaluate information from the studies published between 2011 and 2022. The second goal is to extensively report on the holistic, experimental outcomes of this domain in relation to current research. It is systematically performed to provide a wealthy image and grounded evidence of the current state of research covering EEG-based control of prosthetic arms to all experts and scientists. The third goal is to recognize the gap in knowledge that demands further investigation and to recommend directions for future research in this area.

Publisher

Centre for Evaluation in Education and Science (CEON/CEES)

Subject

General Engineering

Reference62 articles.

1. Abdulrahman Satam, I. 2021. Review Studying of the Latest Development of Prosthetic Limbs Technologies. International Journal of Scientific & Engineering Research, 12(12), pp.721-731 [online]. Available at: https://www.ijser.org/research-paper-publishing-december-2021.aspx [Accessed: 20 November 2022];

2. Acharya, U.R., Hagiwara, Y., Nitin, Deshpande, S.N., Suren, Koh, J.E.W., Oh, S.L., Arunkumar, N., Ciaccio, E.J. & Lim, C.M. 2019. Characterization of focal EEG signals: A review. Future Generation Computer Systems, 91, pp.290-299. Available at: https://doi.org/10.1016/j.future.2018.08.044;

3. Agashe, H.A., Paek, A.Y. & Contreras-Vidal, J.L. 2016. Chapter 4 -Multisession, noninvasive closed-loop neuroprosthetic control of grasping by upper limb amputees. Progress in Brain Research, 228, pp.107-128. Available at: https://doi.org/10.1016/bs.pbr.2016.04.016;

4. Ali, H.A., Goga, N., Vasilateanu, A., Ali, L.A., Abd-Almuhsen, G.S. & Naji, H.K. 2021. A Quantitative Research to Determine User's Requirements for the Mind-Controlled Prosthesis Arm Intelligent System. In: 2021 13th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), Pitesti, Romania, pp.1-8, July 01-03. Available at: https://doi.org/10.1109/ECAI52376.2021.9515168;

5. Beyrouthy, T., Kork, S.A., Korbane, J.A. & Abouelela, M. 2017. EEG Mind Controlled Smart Prosthetic Arm -A Comprehensive Study. Advances in Science, Technology and Engineering Systems Journal, 2(3), pp.891-899. Available at: https://doi.org/10.25046/aj0203111;

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3