Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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DOI：
10.3791/50602
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发表时间：
2013-07-01
影响因子：
1.2
通讯作者：
Contreras-Vidal, Jose L.
中科院分区：
文献类型：
--
作者：
Bulea, Thomas C.;Kilicarslan, Atilla;Contreras-Vidal, Jose L.
最近的研究支持参与脊髓上的网络控制的双足人类行走。部分证据包括研究，包括我们以前的工作，表明跑步机行走期间的步态运动学和肢体协调可以从头皮脑电图（EEG）中推断出来，具有相当高的解码精度。这些结果为非侵入性脑机接口（BMI）系统的发展提供了动力，用于恢复和/或增强步态-康复研究的主要目标。迄今为止，检查步态期间活动的EEG解码的研究仅限于在受控环境中的跑步机行走。然而，为了实际可行，BMI系统必须适用于日常运动任务，例如在地面上行走和转弯。在这里，我们提出了一种新的协议，用于非侵入性收集大脑活动（EEG），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）在跑步机和地面行走任务。通过在不受控环境中收集这些数据，可以了解从头皮EEG解码无约束步态和表面EMG的可行性。
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.