Forenzo Dylan, Liu Yixuan, Kim Jeehyun, Ding Yidan, Yoon Taehyung, He Bin
bioRxiv. 2023 Feb 21:2023.02.20.529307. doi: 10.1101/2023.02.20.529307.
EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control.
We evaluated a cohort of 25 human subjects' ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI).
Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), statistically outperforms MI alone (42%), and was higher, but not statistically significant, than OSA alone (45%). MI+OSA had a similar performance to each subject's best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA.
Integrating MI and OSA leads to improved performance over MI alone at the group level and is the best BCI paradigm option for some subjects.
This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users' BCI performance.
基于脑电图的脑机接口(BCI)是一种用于替代或恢复受损患者运动功能以及实现普通人群大脑与设备直接通信的非侵入性方法。运动想象(MI)是最常用的BCI范式之一,但其性能因个体而异,并且某些用户需要大量训练才能实现控制。在本研究中,我们建议将MI范式与最近提出的 overt 空间注意力(OSA)范式同时整合,以实现BCI控制。
我们评估了25名人类受试者在5次BCI实验中在一维和二维空间中控制虚拟光标的能力。受试者使用了5种不同的BCI范式:单独的MI、单独的OSA、同时针对同一目标的MI和OSA(MI + OSA),以及MI控制一个轴而OSA控制另一个轴(MI/OSA和OSA/MI)。
我们的结果表明,MI + OSA在二维任务中达到了最高的平均在线性能,有效正确率(PVC)为49%,在统计学上优于单独的MI(42%),并且高于单独的OSA(45%),但无统计学意义。MI + OSA的性能与每个受试者在单独的MI和单独的OSA之间的最佳个体方法相似(50%),并且有9名受试者使用MI + OSA达到了他们最高的平均BCI性能。
在群体水平上,将MI和OSA整合可提高性能,并且对于某些受试者来说是最佳BCI范式选择。
这项工作提出了一种新的BCI控制范式,该范式整合了两种现有范式,并通过表明它可以提高用户的BCI性能来证明其价值。