IEEE Trans Biomed Eng. 2024 Jan;71(1):282-294. doi: 10.1109/TBME.2023.3298957. Epub 2023 Dec 22.
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), and statistically outperforms both MI alone (42%) and 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 both individual methods 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)是一种非侵入性方法,可用于替代或恢复受损患者的运动功能,并在普通人群中实现大脑与设备的直接通信。运动想象(MI)是最常用的 BCI 范式之一,但它在个体之间的表现各不相同,某些用户需要大量的培训才能建立控制能力。在这项研究中,我们提出将 MI 范式与最近提出的显性空间注意(OSA)范式相结合,以实现 BCI 控制。
我们评估了 25 名人类受试者在 5 次 BCI 会话中控制一维和二维虚拟光标移动的能力。受试者使用了 5 种不同的 BCI 范式:单独的 MI、单独的 OSA、同时用于相同目标的 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,在二维任务中,MI+OSA 达到了 49%的平均在线性能(Percent Valid Correct,PVC),明显优于单独的 MI(42%)和单独的 OSA(45%)。MI+OSA 的性能与每个受试者在单独的 MI 和 OSA 之间的最佳个体方法相似(50%),9 名受试者使用 MI+OSA 达到了他们的最高平均 BCI 性能。
将 MI 和 OSA 结合起来,在组水平上可以提高两种单独方法的性能,并且对于一些受试者来说是最佳的 BCI 范式选择。
这项工作提出了一种新的 BCI 控制范式,它整合了两种现有的范式,并通过证明它可以提高用户的 BCI 性能来展示其价值。