Abstract
We present a mobile brain-body imaging (MoBI) dataset acquired during treadmill walking in a brain-computer interface (BCI) task. The data were collected from eight healthy subjects, each having three identical trials. Each trial consisted of three conditions: standing, treadmill walking, and treadmill walking with a closed-loop BCI. During the BCI condition, subjects used their brain activity to control a virtual avatar on a screen to walk in real-time. Robust procedures were designed to record lower limb joint angles (bilateral hip, knee, and ankle) using goniometers synchronized with 60-channel scalp electroencephalography (EEG). Additionally, electrooculogram (EOG), EEG electrodes impedance, and digitized EEG channel locations were acquired to aid artifact removal and EEG dipole-source localization. This dataset is unique in that it is the first published MoBI dataset recorded during walking. It is useful in addressing several important open research questions, such as how EEG is coupled with gait cycle during closed-loop BCI, how BCI influences neural activity during walking, and how a BCI decoder may be optimized.
Highlights
Background & SummaryHuman walking is a complex task that involves supraspinal structures in the nervous system[1]
Scalp electroencephalography (EEG) is a portable and relatively easy-to-use means to accomplish this goal by non-invasively measuring electrical brain activity
EEG recording during walking has demonstrated features that differ from those recorded during standing, and more importantly, couple with the gait cycle[6]
Summary
Background & SummaryHuman walking is a complex task that involves supraspinal structures in the nervous system[1]. Data from 60-channel active EEG and goniometer measurements from 6 joint angles (bilateral hip, knee, and ankle) were recorded. The EEG, EOG, and joint angle data were synchronized for subsequent segmentation into gait cycles.
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