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基于运动想象脑电的轮椅运动与控制中的脑机接口:系统文献回顾。

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.

机构信息

Department of Medical and Surgical Sciences, "Magna Græcia" University, 88100 Catanzaro, Italy.

Neuroscience Research Center, Magna Græcia University, 88100 Catanzaro, Italy.

出版信息

Sensors (Basel). 2021 Sep 19;21(18):6285. doi: 10.3390/s21186285.

Abstract

The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.

摘要

2019 年冠状病毒病(COVID-19)大流行紧急情况凸显了创新型辅助器具、设备和辅助技术的必要性,以帮助重度残疾人士过上他们的日常生活。基于脑电图的脑机接口(BCI)可以使面临严重健康挑战的个体提高独立性,促进参与活动,从而提高整体幸福感并预防损伤。本系统综述提供了基于脑电图的 BCI 的最新应用,特别是那些使用运动想象(MI)数据的应用,这些应用涉及轮椅控制和移动。本文对自 2010 年以来开展的不同研究进行了全面检查,重点关注所使用的算法分析、特征提取、特征选择和分类技术,以及轮椅组件和性能评估。本文提供的结果可以突出当前应用于重度残疾人士的生物医学仪器的局限性,并关注创新研究课题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7923/8473300/1db000eebfc7/sensors-21-06285-g001.jpg

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