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):163-179. doi: 10.36518/2689-0216.1196. eCollection 2021.
Description In this review article, we aimed to create a summary of the effects of internal variables on the performance of sensorimotor rhythm-based brain computer interfaces (SMR-BCIs). SMR-BCIs can be potentially used for interfacing between the brain and devices, bypassing usual central nervous system output, such as muscle activity. The careful consideration of internal factors, affecting SMR-BCI performance, can maximize BCI application in both healthy and disabled people. Internal variables may be generalized as descriptors of the processes mainly dependent on the BCI user and/or originating within the user. The current review aimed to critically evaluate and summarize the currently accumulated body of knowledge regarding the effect of internal variables on SMR-BCI performance. The examples of such internal variables include motor imagery, hand coordination, attention, motivation, quality of life, mood and neurophysiological signals other than SMR. We will conclude our review with the discussion about the future developments regarding the research on the effects of internal variables on SMR-BCI performance. The end-goal of this review paper is to provide current BCI users and researchers with the reference guide that can help them optimize the SMR-BCI performance by accounting for possible influences of various internal factors.
摘要 在这篇综述文章中,我们旨在总结内部变量对基于感觉运动节律的脑机接口(SMR-BCI)性能的影响。SMR-BCI可潜在地用于大脑与设备之间的接口,绕过诸如肌肉活动等通常的中枢神经系统输出。仔细考虑影响SMR-BCI性能的内部因素,可以最大限度地提高BCI在健康人和残疾人中的应用。内部变量可以概括为主要取决于BCI用户和/或源自用户内部的过程的描述符。当前的综述旨在批判性地评估和总结目前积累的关于内部变量对SMR-BCI性能影响的知识体系。此类内部变量的例子包括运动想象、手部协调性、注意力、动机、生活质量、情绪以及除SMR之外的神经生理信号。我们将通过讨论内部变量对SMR-BCI性能影响的研究的未来发展来结束我们的综述。这篇综述文章的最终目标是为当前的BCI用户和研究人员提供一份参考指南,帮助他们通过考虑各种内部因素的可能影响来优化SMR-BCI性能。