A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery

TL;DR

MIND, a public motor imagery fNIRS-EEG dataset based on a four-class directional MI paradigm of the right upper limb, is constructed and analysed to validate the potential advantages of hybrid fNIRS-EEG BCIs in terms of classification accuracy.

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.

Figures (12)

• Figure 1: Motivation framework of the dataset. (a) MI subregions: motor–somatosensory–parietal network (M1/S1, PMd/PMv, SMA, and SPL/IPS). (b) Contralateral Activation Maps of Left/Right-hand. (c) Activation maps of different subspaces under unilateral limb. (d) Mechanism of EEG recording. Due to volume conduction, EEG has low spatial resolution. (e) Mechanism of fNIRS recording. fNIRS measures local $\Delta$HbO/$\Delta$HbR with higher spatial specificity. All activation maps are plotted on a common cortical surface; brighter colors indicate stronger MI-related activity.
• Figure 2: Representations of visual instruction to each unilateral limb task. (a) horizontal movement from left to right. (b) vertical movement from top to bottom. (c) diagonal movement from upper left to lower right. (d) diagonal movement from upper right to lower left (e) rest.
• Figure 3: Experimental Paradigm of unilateral limb MI. Each subject completed three modules, with two sessions in each module, namely horizontal, vertical and two types of diagonal movements, totaling six sessions. Each session included a resting state and 20 MI sessions, making a total of 120 task sessions.
• Figure 4: The instrumentation used in eeg data collection. (a) The EEG cap and signal amplifier. (b) The electrode positions on the EEG cap.
• Figure 5: Directory structure of the raw multimodal fNIRS--EEG dataset.