Neural decoding of treadmill walking from noninvasive electroencephalographic signals
Neural decoding of treadmill walking from noninvasive electroencephalographic signals
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DOI：
10.1152/jn.00104.2011
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发表时间：
2011-10-01
影响因子：
2.5
通讯作者：
Contreras-Vidal, Jose Luis
中科院分区：
文献类型：
--
作者：
Presacco, Alessandro;Goodman, Ronald;Contreras-Vidal, Jose Luis
President A，Goodman R，Forrester L，Contreras-Vidal JL.从非侵入性脑电图信号对踏车行走的神经解码。J Neurophysiol 106：1875-1887，2011.首次发表于2011年7月13日; doi：10.1152/jn.00104.2011.-来自恒河猴的初级运动和躯体感觉区域中的皮质神经元集合的慢性记录提供了关于双足运动的准确信息（Fitzsimmons NA，Lebedev MA，Peikon ID，Nicolelis MA. Front Integr Neurosci 3：3，2009）。在这里，我们表明，在正常和精密（注意）人类跑步机行走的踝关节，膝关节和髋关节的线性和角度运动学可以推断出非侵入性头皮脑电图（EEG）与解码精度相媲美的神经解码器的基础上记录在非人类灵长类动物的多个单单位活动（SUA）。记录了6名健康成年人。参与者被要求以他们自己选择的舒适速度在跑步机上行走，同时接收他们下肢的视觉反馈（即，精确行走），以反复避免踩到在跑步机带上画出的条带上。记录左右髋关节、膝关节和踝关节的角度和线性运动学以及EEG，并设计神经解码器，并使用交叉验证程序进行优化。值得注意的是，这些解码器的最佳电极组也用于准确地推断正常行走任务中的步态轨迹，而不需要受试者控制和监测他们的脚的位置。我们的研究结果表明，高参与额-后皮质网络的控制精度和正常的行走，并建议EEG信号可用于研究在真实的时间行走的皮质动力学和开发脑机接口，旨在恢复人类的步态功能。
Presacco A, Goodman R, Forrester L, Contreras-Vidal JL. Neural decoding of treadmill walking from noninvasive electroencephalographic signals. J Neurophysiol 106: 1875-1887, 2011. First published July 13, 2011; doi: 10.1152/jn.00104.2011.-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.