MIND：A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery

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 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 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.