TY - JOUR
T1 - A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface
T2 - A mobile brainbody imaging dataset recorded during treadmill walking with a brain-computer interface
AU - He, Yongtian
AU - Luu, Trieu Phat
AU - Nathan, Kevin
AU - Nakagome, Sho
AU - Contreras-Vidal, Jose L.
N1 - Funding Information:
This research was supported by award R01NS075889 from the National Institute of Neurological Disorders and Stroke (NINDS). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NINDS or the National Institutes of Health (NIH). The authors of this manuscript had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. The authors ask that any researcher who uses this data cites this article in any resulting presentation or publication.
Publisher Copyright:
© The Author(s) 2018.
PY - 2018/4/24
Y1 - 2018/4/24
N2 - 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.
AB - 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.
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U2 - 10.1038/sdata.2018.74
DO - 10.1038/sdata.2018.74
M3 - Article
C2 - 29688217
AN - SCOPUS:85045940618
SN - 2052-4463
VL - 5
SP - 180074
JO - Scientific Data
JF - Scientific Data
M1 - 180074
ER -