MIT Media Lab, Cambridge, MA, United States of America.
Inria Rennes, Rennes, France.
PLoS One. 2019 Jan 3;14(1):e0210145. doi: 10.1371/journal.pone.0210145. eCollection 2019.
Brain-Computer Interfaces (BCIs) have become more and more popular these last years. Researchers use this technology for several types of applications, including attention and workload measures but also for the direct control of objects by the means of BCIs. In this work we present a first, multidimensional feature space for EEG-based BCI applications to help practitioners to characterize, compare and design systems, which use EEG-based BCIs. Our feature space contains 4 axes and 9 sub-axes and consists of 41 options in total as well as their different combinations. We presented the axes of our feature space and we positioned our feature space regarding the existing BCI and HCI taxonomies and we showed how our work integrates the past works, and/or complements them.
近年来,脑机接口(BCI)越来越受欢迎。研究人员将这项技术应用于多种类型的应用,包括注意力和工作负荷的测量,也包括通过 BCI 直接控制物体。在这项工作中,我们提出了一个用于基于 EEG 的 BCI 应用的多维特征空间,以帮助从业者对使用基于 EEG 的 BCI 的系统进行特征描述、比较和设计。我们的特征空间包含 4 个轴和 9 个子轴,总共包含 41 个选项及其不同的组合。我们介绍了特征空间的轴,并将其定位在现有的 BCI 和 HCI 分类法中,展示了我们的工作如何整合过去的工作,并对其进行补充。