IEEE Trans Neural Syst Rehabil Eng. 2022;30:476-485. doi: 10.1109/TNSRE.2022.3154707. Epub 2022 Mar 8.
Naturally occurring postural instabilities that occur while standing and walking elicit specific cortical responses in the fronto-central regions (N1 potentials) followed by corrective balance responses to prevent falling. However, no framework could simultaneously track different biomechanical parameters preceding N1s, predict N1s, and assess their predictive power. Here, we propose a framework and show its utility by examining cortical activity (through electroencephalography [EEG]), ground reaction forces, and head acceleration in the anterior-posterior (AP) direction. Ten healthy young adults carried out a balance task of standing on a support surface with or without sway referencing in the AP direction, amplifying, or dampening natural body sway. Using independent components from the fronto-central cortical region obtained from subject-specific head models, we first robustly validated a prior approach on identifying low-amplitude N1 potentials before early signs of balance corrections. Then, a machine learning algorithm was used to evaluate different biomechanical parameters obtained before N1 potentials, to predict the occurrence of N1s. When different biomechanical parameters were directly compared, the time to boundary (TTB) was found to be the best predictor of the occurrence of upcoming low-amplitude N1 potentials during a balance task. Based on these findings, we confirm that the spatio-temporal characteristics of the center of pressure (COP) might serve as an essential parameter that can facilitate the early detection of postural instability in a balance task. Extending our framework to identify such biomarkers in dynamic situations like walking might improve the implementation of corrective balance responses through brain-machine-interfaces to reduce falls in the elderly.
站立和行走时自然出现的姿势不稳定会在前额中央区域引起特定的皮质反应（N1 电位），然后会产生纠正平衡的反应以防止跌倒。然而，目前还没有一个框架可以同时跟踪 N1 之前的不同生物力学参数、预测 N1 并评估其预测能力。在这里，我们提出了一个框架，并通过检查皮质活动（通过脑电图 [EEG]）、地面反力和头部在前后（AP）方向的加速度来展示其效用。10 名健康的年轻成年人在没有或有 AP 方向的摆动参考、放大或抑制自然身体摆动的情况下，在支撑表面上进行平衡任务。使用从特定于个体的头部模型获得的额中央皮质区域的独立成分，我们首先稳健地验证了一种先前的方法，用于在平衡纠正的早期迹象之前识别低幅度的 N1 电位。然后，使用机器学习算法来评估 N1 电位之前获得的不同生物力学参数，以预测 N1 的发生。当直接比较不同的生物力学参数时，发现时间到边界（TTB）是预测即将到来的低幅度 N1 电位在平衡任务中发生的最佳预测指标。基于这些发现，我们确认了压力中心（COP）的时空特征可能是作为一个重要参数，可以促进平衡任务中姿势不稳定的早期检测。将我们的框架扩展到识别行走等动态情况下的此类生物标志物，可能会通过脑机接口改善纠正平衡反应的实施，以减少老年人跌倒。
