Bulea Thomas C, Kilicarslan Atilla, Ozdemir Recep, Paloski William H, Contreras-Vidal Jose L
Functional and Applied Biomechanics Group, National Institutes of Health, Bethesda, MD, USA.
J Vis Exp. 2013 Jul 26(77):50602. doi: 10.3791/50602.
Recent studies support the involvement of supraspinal networks in control of bipedal human walking. Part of this evidence encompasses studies, including our previous work, demonstrating that gait kinematics and limb coordination during treadmill walking can be inferred from the scalp electroencephalogram (EEG) with reasonably high decoding accuracies. These results provide impetus for development of non-invasive brain-machine-interface (BMI) systems for use in restoration and/or augmentation of gait- a primary goal of rehabilitation research. To date, studies examining EEG decoding of activity during gait have been limited to treadmill walking in a controlled environment. However, to be practically viable a BMI system must be applicable for use in everyday locomotor tasks such as over ground walking and turning. Here, we present a novel protocol for non-invasive collection of brain activity (EEG), muscle activity (electromyography (EMG)), and whole-body kinematic data (head, torso, and limb trajectories) during both treadmill and over ground walking tasks. By collecting these data in the uncontrolled environment insight can be gained regarding the feasibility of decoding unconstrained gait and surface EMG from scalp EEG.
最近的研究支持脊髓上网络参与对人类双足行走的控制。部分证据包括一些研究，包括我们之前的工作，这些研究表明，在跑步机行走过程中的步态运动学和肢体协调性可以通过头皮脑电图（EEG）以相当高的解码精度推断出来。这些结果为开发用于恢复和/或增强步态的非侵入性脑机接口（BMI）系统提供了动力——这是康复研究的一个主要目标。迄今为止，研究步态期间活动的脑电图解码仅限于在受控环境中的跑步机行走。然而，要在实际中可行，BMI系统必须适用于日常运动任务，如地面行走和转弯。在这里，我们提出了一种新颖的方案，用于在跑步机和地面行走任务期间非侵入性地收集大脑活动（脑电图）、肌肉活动（肌电图（EMG））和全身运动学数据（头部、躯干和肢体轨迹）。通过在不受控制的环境中收集这些数据，可以了解从头皮脑电图解码无约束步态和表面肌电图的可行性。
