Neural Engineering and Smart Prosthetics Research Laboratory, Department of Kinesiology, School of Public Health, University of Maryland, College Park, MD 20742, USA.
J Neurophysiol. 2011 Oct;106(4):1875-87. doi: 10.1152/jn.00104.2011. Epub 2011 Jul 13.
Chronic recordings from ensembles of cortical neurons in primary motor and somatosensory areas in rhesus macaques provide accurate information about bipedal locomotion (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009). Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function.
恒河猴初级运动和体感皮层神经元集合的慢性记录为双足运动提供了准确的信息 (Fitzsimmons NA, Lebedev MA, Peikon ID, Nicolelis MA. Front Integr Neurosci 3: 3, 2009)。在这里，我们展示了正常和精确（注意）人类跑步机行走过程中踝关节、膝关节和髋关节的线性和角度运动可以从非侵入性头皮脑电图 (EEG) 中推断出来，解码精度可与基于非人类灵长类动物记录的多个单单元活动 (SUAs) 的神经解码器相媲美。记录了六名健康成年人。要求参与者在跑步机上以自己选择的舒适速度行走，同时接受下肢的视觉反馈（即精确行走），以反复避免踩到跑步机皮带上绘制的条纹。记录了左、右髋关节、膝关节和踝关节的角度和线性运动学以及 EEG，并通过交叉验证程序设计和优化了神经解码器。值得注意的是，这些解码器的最佳电极集也用于准确推断不需要受试者控制和监测其脚位的正常行走任务中的步态轨迹。我们的结果表明，额后皮质网络高度参与了精确和正常行走的控制，并表明 EEG 信号可用于实时研究行走的皮质动力学，并开发旨在恢复人类步态功能的脑机接口。
