Horowitz Alex J, Guger Christoph, Korostenskaja Milena
Functional Brain Mapping and Brain Computer Interface Lab, Neuroscience Institute, AdventHealth Orlando, Orlando, FL, USA.
University of Central Florida/HCA Healthcare GME Consortium, Gainesville, Florida.
HCA Healthc J Med. 2021 Jun 28;2(3):143-162. doi: 10.36518/2689-0216.1188. eCollection 2021.
Description Sensorimotor rhythm-based brain-computer interfaces (SMR-BCIs) are used for the acquisition and translation of motor imagery-related brain signals into machine control commands, bypassing the usual central nervous system output. The selection of optimal external variable configuration can maximize SMR-BCI performance in both healthy and disabled people. This performance is especially important now when the BCI is targeted for everyday use in the environment beyond strictly regulated laboratory settings. In this review article, we summarize and critically evaluate the current body of knowledge pertaining to the effect of the external variables on SMR-BCI performance. When assessing the relationship between SMR-BCI performance and external variables, we broadly characterize them as elements that are less dependent on the BCI user and originate from beyond the user. These elements include such factors as BCI type, distractors, training, visual and auditory feedback, virtual reality and magneto electric feedback, proprioceptive and haptic feedback, carefulness of electroencephalography (EEG) system assembling and positioning of EEG electrodes as well as recording-related artifacts. At the end of this review paper, future developments are proposed regarding the research into the effects of external variables on SMR-BCI performance. We believe that our critical review will be of value for academic BCI scientists and developers and clinical professionals working in the field of BCIs as well as for SMR-BCI users.
基于感觉运动节律的脑机接口(SMR-BCI)用于获取与运动想象相关的脑信号并将其转换为机器控制命令,从而绕过通常的中枢神经系统输出。选择最佳的外部变量配置可以在健康人和残疾人中最大限度地提高SMR-BCI的性能。当脑机接口目标是在严格规范的实验室环境之外的日常环境中使用时,这种性能尤为重要。在这篇综述文章中,我们总结并批判性地评估了当前关于外部变量对SMR-BCI性能影响的知识体系。在评估SMR-BCI性能与外部变量之间的关系时,我们将它们大致描述为较少依赖脑机接口用户且源自用户之外的元素。这些元素包括脑机接口类型、干扰因素、训练、视觉和听觉反馈、虚拟现实和磁电反馈、本体感觉和触觉反馈、脑电图(EEG)系统组装的精细程度以及EEG电极的定位以及与记录相关的伪迹等因素。在这篇综述文章的结尾,针对外部变量对SMR-BCI性能影响的研究提出了未来的发展方向。我们相信,我们的批判性综述对于学术脑机接口科学家和开发者、从事脑机接口领域工作的临床专业人员以及SMR-BCI用户都将具有价值。