ArticleScientific data2026
A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery.Scientific data · 2026Article
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10 authors.
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Abstract
Unilateral limb motor imagery (MI) plays an important role in upper-limb motor rehabilitation and precise control of external devices, and places higher demands on spatial resolution. However, most existing public datasets focus on binary- or four-class left-right limb paradigms that mainly exploit coarse hemispheric lateralization, and there is still a lack of multimodal datasets that simultaneously record electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for unilateral multi-directional MI. To address this gap, we present MIND, a public motor imagery fNIRS-EEG dataset based on a four-class directional MI paradigm of the right upper limb. The dataset includes 64-channel EEG recordings (1000 Hz) and 51-channel fNIRS recordings (47.62 Hz) from 30 participants (12 females, 18 males; aged 19.0-25.0 years). We analyze the spatiotemporal characteristics of EEG spectral power and hemodynamic responses, and provide baseline classification summaries for EEG, fNIRS, and combined modalities as technical validation of task-related information in the dataset. We expect that this dataset will facilitate the evaluation and comparison of neuroimaging analysis and decoding methods.
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