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<p>As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG). Movement imagination of multi-types of joints from the same limb allows the development of more accurate and intuitive BCI systems. In this work, we reported an open dataset including EEG and functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects performing eight MI tasks from four types of joints including hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, and shoulder flexion/extension, resulting in a total of 5760 trials. The validity of multi-modal data was verified both from the EEG/fNIRS activation patterns and the classification performance. It is expected that this dataset will facilitate the development and innovation of decoding algorithms for MI of multi-types of joints based on multi-modal EEG-fNIRS data.</p>

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A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月6日

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  • Weibo Yi,
  • Jiaming Chen,
  • Dan Wang,
  • Xinkang Hu,
  • Meng Xu,
  • Fangda Li,
  • Shuhan Wu,
  • Jin Qian

摘要

As one of the important brain-computer interface (BCI) paradigms, motor imagery (MI) enables the control of external devices via identification of motor intention by decoding the features of Electroencephalography (EEG). Movement imagination of multi-types of joints from the same limb allows the development of more accurate and intuitive BCI systems. In this work, we reported an open dataset including EEG and functional near-infrared spectroscopy (fNIRS) recordings from 18 subjects performing eight MI tasks from four types of joints including hand open/close, wrist flexion/extension, wrist abduction/adduction, elbow pronation/supination, elbow flexion/extension, shoulder pronation/supination, shoulder abduction/adduction, and shoulder flexion/extension, resulting in a total of 5760 trials. The validity of multi-modal data was verified both from the EEG/fNIRS activation patterns and the classification performance. It is expected that this dataset will facilitate the development and innovation of decoding algorithms for MI of multi-types of joints based on multi-modal EEG-fNIRS data.