Sujatha Ravindran Akshay, Malaya Christopher A, John Isaac, Francisco Gerard E, Layne Charles, Contreras-Vidal Jose L
Noninvasive Brain-Machine Interface System Laboratory, Department of Electrical and Computer Engineering, University of Houston, Houston, Texas, 77204, United States of America.
IUCRC BRAIN, University of Houston, Houston, Texas, 77204, United States of America.
J Neural Eng. 2022 May 26;19(3). doi: 10.1088/1741-2552/ac6ca9.
Falls are a leading cause of death in adults 65 and older. Recent efforts to restore lower-limb function in these populations have seen an increase in the use of wearable robotic systems; however, fall prevention measures in these systems require early detection of balance loss to be effective. Prior studies have investigated whether kinematic variables contain information about an impending fall, but few have examined the potential of using electroencephalography (EEG) as a fall-predicting signal and how the brain responds to avoid a fall.To address this, we decoded neural activity in a balance perturbation task while wearing an exoskeleton. We acquired EEG, electromyography (EMG), and center of pressure (COP) data from seven healthy participants during mechanical perturbations while standing. The timing of the perturbations was randomized in all trials.We found perturbation evoked potentials (PEP) components as early as 75-134 ms after the onset of the external perturbation, which preceded both the peak in EMG (∼180 ms) and the COP (∼350 ms). A convolutional neural network trained to predict balance perturbations from single-trial EEG had a mean F-score of 75.0 ± 4.3%. Clustering GradCAM-based model explanations demonstrated that the model utilized components in the PEP and was not driven by artifacts. Additionally, dynamic functional connectivity results agreed with model explanations; the nodal connectivity measured using phase difference derivative was higher in the occipital-parietal region in the early stage of perturbations, before shifting to the parietal, motor, and back to the frontal-parietal channels. Continuous-time decoding of COP trajectories from EEG, using a gated recurrent unit model, achieved a mean Pearson's correlation coefficient of 0.7 ± 0.06.Overall, our findings suggest that EEG signals contain short-latency neural information related to an impending fall, which may be useful for developing brain-machine interface systems for fall prevention in robotic exoskeletons.
跌倒在65岁及以上的成年人中是主要的死亡原因。最近，为恢复这些人群的下肢功能所做的努力使得可穿戴机器人系统的使用有所增加；然而，这些系统中的跌倒预防措施需要早期检测到平衡丧失才能有效。先前的研究调查了运动学变量是否包含有关即将跌倒的信息，但很少有人研究使用脑电图（EEG）作为跌倒预测信号的潜力以及大脑如何做出反应以避免跌倒。为了解决这个问题，我们在穿着外骨骼时对平衡扰动任务中的神经活动进行了解码。我们在站立时的机械扰动期间从七名健康参与者那里获取了脑电图、肌电图（EMG）和压力中心（COP）数据。在所有试验中，扰动的时间是随机的。我们发现，早在外部扰动开始后75 - 134毫秒就出现了扰动诱发电位（PEP）成分，这比肌电图峰值（约180毫秒）和压力中心峰值（约350毫秒）都要早。一个经过训练从单次试验脑电图预测平衡扰动的卷积神经网络的平均F分数为75.0 ± 4.3%。基于聚类GradCAM的模型解释表明，该模型利用了PEP中的成分，而不是由伪迹驱动的。此外，动态功能连接结果与模型解释一致；使用相位差导数测量的节点连接性在扰动早期的枕顶区域较高，然后转移到顶叶、运动区域，再回到额顶通道。使用门控循环单元模型从脑电图对压力中心轨迹进行连续时间解码，平均皮尔逊相关系数达到了0.7 ± 0.06。总体而言，我们的研究结果表明，脑电图信号包含与即将跌倒相关的短潜伏期神经信息，这可能有助于开发用于机器人外骨骼跌倒预防的脑机接口系统。
