Laboratory for Non-Invasive Brain Machine Interfaces, Department of Electrical & Computer Engineering, University of Houston, Houston, TX 77056, USA.
Sci Data. 2018 Jul 10;5:180133. doi: 10.1038/sdata.2018.133.
Human locomotion is a complex process that requires the integration of central and peripheral nervous signalling. Understanding the brain's involvement in locomotion is challenging and is traditionally investigated during locomotor imagination or observation. However, stationary imaging methods lack the ability to infer information about the peripheral and central signalling during actual task execution. In this report, we present a dataset containing simultaneously recorded electroencephalography (EEG), lower-limb electromyography (EMG), and full body motion capture recorded from ten able-bodied individuals. The subjects completed an average of twenty trials on an experimental gait course containing level-ground, ramps, and stairs. We recorded 60-channel EEG from the scalp and 4-channel EOG from the face and temples. Surface EMG was recorded from six muscle sites bilaterally on the thigh and shank. The motion capture system consisted of seventeen wireless IMUs, allowing for unconstrained ambulation in the experimental space. In this report, we present the rationale for collecting these data, a detailed explanation of the experimental setup, and a brief validation of the data quality.
人类运动是一个复杂的过程，需要中枢和外周神经信号的整合。理解大脑在运动中的参与是具有挑战性的，传统上是通过运动想象或观察来研究的。然而，静止成像方法缺乏推断实际任务执行过程中周围和中枢信号的能力。在本报告中，我们介绍了一个数据集，其中包含同时记录的脑电图 (EEG)、下肢肌电图 (EMG) 和来自十个健全个体的全身运动捕捉。受试者在包含平地、斜坡和楼梯的实验步态路线上平均完成了二十次试验。我们从头皮记录了 60 通道 EEG，从面部和太阳穴记录了 4 通道 EOG。表面肌电图从大腿和小腿的双侧六个肌肉部位记录。运动捕捉系统由十七个无线 IMU 组成，允许在实验空间中不受限制地行走。在本报告中，我们介绍了收集这些数据的基本原理，详细解释了实验设置，并简要验证了数据质量。
