A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery

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.