A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery

TL;DR

This paper introduces MIND, a comprehensive multimodal dataset combining EEG and fNIRS recordings during unilateral limb motor imagery, aiming to advance neuroimaging analysis and brain-computer interface development.

Contribution

The paper presents a novel multimodal dataset with four-class directional MI, filling a gap in existing datasets and enabling more precise BCI research.

Findings

Analysis of EEG spectral power and hemodynamic responses

Validation of hybrid fNIRS-EEG BCIs for classification accuracy

Dataset facilitates evaluation of neuroimaging decoding methods

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.