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A mobile brain-body imaging dataset recorded during treadmill walking with a brain-computer interface.

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This study introduces a novel mobile brain-body imaging dataset of treadmill walking with a brain-computer interface (BCI). The data capture brain and body movement, advancing research in BCI for gait control.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Mobile brain-body imaging (MoBI) integrates brain activity recording with full-body motion capture.
  • Brain-computer interfaces (BCI) offer potential for controlling external devices using neural signals.
  • Understanding brain-body dynamics during locomotion is crucial for developing advanced assistive technologies.

Purpose of the Study:

  • To present the first published MoBI dataset collected during treadmill walking.
  • To investigate neural correlates of gait control within a closed-loop BCI paradigm.
  • To provide a resource for optimizing BCI decoders for real-time locomotion tasks.

Main Methods:

  • Acquisition of synchronized 60-channel scalp electroencephalography (EEG) and lower limb joint angles (goniometers) during treadmill walking.
  • Inclusion of electrooculogram (EOG) and EEG impedance for artifact correction and source localization.
  • Experimental design with three conditions: standing, treadmill walking, and BCI-controlled treadmill walking.

Main Results:

  • The dataset contains synchronized brain and lower limb kinematic data from eight healthy subjects across multiple trials and conditions.
  • The closed-loop BCI condition allowed subjects to control a virtual avatar's gait in real-time using their EEG signals.
  • Data preprocessing steps included artifact removal and channel localization for robust analysis.

Conclusions:

  • This unique MoBI dataset during walking provides a valuable resource for studying brain-gait interactions.
  • It enables research into how BCIs influence neural activity during locomotion.
  • The dataset can facilitate the development and optimization of BCI systems for gait rehabilitation and control.