Robinson Neethu, Chouhan Tushar, Mihelj Ernest, Kratka Paulina, Debraine Frédéric, Wenderoth Nicole, Guan Cuntai, Lehner Rea
School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
Future Health Technologies, Singapore-ETH Centre, Singapore, Singapore.
Front Hum Neurosci. 2021 Jun 15;15:648275. doi: 10.3389/fnhum.2021.648275. eCollection 2021.
Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms ( = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.
最近的几项研究已经证明了脑机接口（BCI）技术如何揭示各种任务背后的神经机制，并将其转化为控制命令。虽然大量研究已经证明了BCI的理论潜力，但一个令人担忧的问题是，这些研究仍然局限于实验室环境，并且大多限于健康、身体健全的受试者。2020年CYBATHLON BCI竞赛为进一步开发BCI设计策略提供了一个机会，以便将其用于与四肢瘫痪终端用户的实时应用中。在本研究中，作为参加2020年CYBATHLON BCI竞赛准备工作的一部分，我们研究了BCI在其组件选择方面的设计问题，特别是校准范式的类型及其与长期使用的相关性。最终目标是开发一个适合长期使用的用户友好且引人入胜的界面，特别是针对脊髓损伤（SCI）患者。我们使用预训练的BCI解码器，将传统的开环校准范式与实时闭环范式的功效进行了比较。本研究分析了各种性能指标，包括由此产生的分类性能、游戏完成时间、大脑激活图，以及来自试点的主观反馈。我们的结果表明，具有实时反馈的闭环校准范式对试点更具吸引力。与传统校准范式相比，它们还显示出实现更好的在线中位数分类性能的迹象（ = 0.0008）。我们还观察到，在实验界面与最终应用非常相似的闭环范式中，会引发更强且更局部化的大脑激活模式。因此，基于对单受试者数据的这种纵向评估，我们证明了基于BCI的校准范式，如具有实时反馈的主动用户参与范式，有助于实现更好的用户接受度和性能。
