Applied Neurotechnology Lab, Department of Psychiatry and Psychotherapy, University Hospital of Tübingen, Tübingen, Germany.
Clinical Neurotechnology Lab, Neuroscience Research Center (NWFZ), Department of Psychiatry and Psychotherapy, Charité - University Medicine Berlin, Berlin, Germany.
J Neural Eng. 2021 Jul 23;18(4). doi: 10.1088/1741-2552/ac1177.
Voluntary control of sensorimotor rhythms (SMRs, 8-12 Hz) can be used for brain-computer interface (BCI)-based operation of an assistive hand exoskeleton, e.g. in finger paralysis after stroke. To gain SMR control, stroke survivors are usually instructed to engage in motor imagery (MI) or to attempt moving the paralyzed fingers resulting in task- or event-related desynchronization (ERD) of SMR (SMR-ERD). However, as these tasks are cognitively demanding, especially for stroke survivors suffering from cognitive impairments, BCI control performance can deteriorate considerably over time. Therefore, it would be important to identify biomarkers that predict decline in BCI control performance within an ongoing session in order to optimize the man-machine interaction scheme.Here we determine the link between BCI control performance over time and heart rate variability (HRV). Specifically, we investigated whether HRV can be used as a biomarker to predict decline of SMR-ERD control across 17 healthy participants using Granger causality. SMR-ERD was visually displayed on a screen. Participants were instructed to engage in MI-based SMR-ERD control over two consecutive runs of 8.5 min each. During the 2nd run, task difficulty was gradually increased.While control performance (= .18) and HRV (= .16) remained unchanged across participants during the 1st run, during the 2nd run, both measures declined over time at high correlation (performance: -0.61%/10 s,= 0; HRV: -0.007 ms/10 s,< .001). We found that HRV exhibited predictive characteristics with regard to within-session BCI control performance on an individual participant level (< .001).These results suggest that HRV can predict decline in BCI performance paving the way for adaptive BCI control paradigms, e.g. to individualize and optimize assistive BCI systems in stroke.
自愿控制感觉运动节律（SMR，8-12Hz）可用于基于脑机接口（BCI）操作辅助手外骨骼，例如在中风后手指瘫痪的情况下。为了获得 SMR 控制，中风幸存者通常被指示进行运动想象（MI）或尝试移动瘫痪的手指，从而导致 SMR 的任务或事件相关去同步（ERD）（SMR-ERD）。然而，由于这些任务认知要求较高，尤其是对于患有认知障碍的中风幸存者来说，BCI 控制性能可能会随着时间的推移而显著下降。因此，识别出在正在进行的会话中预测 BCI 控制性能下降的生物标志物非常重要，以便优化人机交互方案。在这里，我们确定了随着时间的推移 BCI 控制性能与心率变异性（HRV）之间的联系。具体来说，我们使用格兰杰因果关系调查了 HRV 是否可以用作生物标志物，以预测 17 名健康参与者在两次连续的 8.5 分钟运行中 SMR-ERD 控制的下降情况。SMR-ERD 在屏幕上可视显示。参与者被指示在两个连续的 8.5 分钟运行中进行基于 MI 的 SMR-ERD 控制。在第二个运行期间，任务难度逐渐增加。虽然在第一个运行期间，控制性能（=.18）和 HRV（=.16）在参与者之间保持不变，但在第二个运行期间，这两个指标都随着时间的推移而以高相关性下降（性能：-0.61%/10s，= 0；HRV：-0.007 ms/10 s，<.001）。我们发现 HRV 在个体参与者水平上表现出对会话内 BCI 控制性能的预测特征（<.001）。这些结果表明，HRV 可以预测 BCI 性能的下降，为自适应 BCI 控制范式铺平道路，例如在中风中实现辅助 BCI 系统的个体化和优化。
