ArticleScientific data2025
A multi-modal dataset of electroencephalography and functional near-infrared spectroscopy recordings for motor imagery of multi-types of joints from unilateral upper limb.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of Artificial Intelligence in the Classification of Upper-Limb Motion Using EEG and EMG Signals: A Review.Sensors (Basel, Switzerland) · 2026Pooled it
- The Digital Transformation of Rehabilitation Medicine: A Narrative Review of Artificial Intelligence Innovations, Clinical Integration, and Future Paradigms.Journal of evaluation in clinical practice · 2026Review
- A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery.Scientific data · 2026Article
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Authors and funding
8 authors.
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Abstract
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.
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