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arXiv · 2602.04299

A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery

Lufeng Feng·Baomin Xu·Haoran Zhang·Bihai Lin·Zuxuan Deng·Sidi Tao·Chenyu Liu·Shifan Jia·Li Duan·Ziyu Jia
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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 EEG and fNIRS for unilateral multi-directional MI. To address this gap, we constructed 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 analyse the spatiotemporal characteristics of EEG spectral power and hemodynamic responses, and validate the potential advantages of hybrid fNIRS-EEG BCIs in terms of classification accuracy. We expect that this dataset will facilitate the evaluation and comparison of neuroimaging analysis and decoding methods.

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Lufeng Feng, Baomin Xu, Haoran Zhang, Bihai Lin, Zuxuan Deng, Sidi Tao, Chenyu Liu, Shifan Jia, Li Duan, Ziyu Jia. 2026-02-04. A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery. https://arxiv.org/abs/2602.04299

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