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<p>Brain-machine interfaces (BMIs) translate brain signals into motor commands for assistive devices. Despite significant advances, the long-term effects of BMI training on neural adaptation, classifier stability, and individual variability remain poorly understood. We present a multimodal, longitudinal dataset collected from seven healthy participants over nine sessions spanning 15 to 81 days. The dataset includes high-density electroencephalography (EEG), electrooculography (EOG), inertial measurement unit (IMU) data, and exoskeleton state information during BMI control. During the open-loop training phase, participants performed kinesthetic motor imagery (KI) while a remotely controlled exoskeleton executed walking and stopping commands. After the open loop training phase, the system transitioned to closed loop BMI control. For closed-loop control, lower delta band EEG signals were classified using Local Fisher Discriminant Analysis and a Gaussian Mixture Model. The classifier was continuously updated using open-loop data from Sessions#1-5, after which its parameters were fixed. The dataset also includes post-experiment MRI scans from five participants performing KI while viewing themselves walking in the exoskeleton. This report outlines the experimental setup, data collection, and preliminary validation, providing a resource for future BMI research.</p>

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EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月1日

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  • Shantanu Sarkar,
  • Kevin Nathan,
  • Atilla Kilicarslan,
  • David Eguren,
  • Robert Grossman,
  • Jose L. Contreras-Vidal

摘要

Brain-machine interfaces (BMIs) translate brain signals into motor commands for assistive devices. Despite significant advances, the long-term effects of BMI training on neural adaptation, classifier stability, and individual variability remain poorly understood. We present a multimodal, longitudinal dataset collected from seven healthy participants over nine sessions spanning 15 to 81 days. The dataset includes high-density electroencephalography (EEG), electrooculography (EOG), inertial measurement unit (IMU) data, and exoskeleton state information during BMI control. During the open-loop training phase, participants performed kinesthetic motor imagery (KI) while a remotely controlled exoskeleton executed walking and stopping commands. After the open loop training phase, the system transitioned to closed loop BMI control. For closed-loop control, lower delta band EEG signals were classified using Local Fisher Discriminant Analysis and a Gaussian Mixture Model. The classifier was continuously updated using open-loop data from Sessions#1-5, after which its parameters were fixed. The dataset also includes post-experiment MRI scans from five participants performing KI while viewing themselves walking in the exoskeleton. This report outlines the experimental setup, data collection, and preliminary validation, providing a resource for future BMI research.