用于单侧肢体运动想象的多模态fNIRS-EEG数据集
A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery
AI总结：
本文提出MIND数据集，用于单侧肢体多方向运动想象，通过融合fNIRS和EEG数据提升BCI分类性能。
摘要：
单侧肢体运动想象（MI）在上肢运动康复和外部设备的精确控制中起着重要作用，并对空间分辨率提出了更高的要求。然而，大多数现有的公开数据集主要关注二元或四类左右肢体范式，主要利用粗略的半球侧化，仍缺乏同时记录EEG和fNIRS的多模态数据集，用于单方向的单侧运动想象。为解决这一差距，我们构建了MIND，一个基于右上肢四类方向性运动想象范式的公共运动想象fNIRS-EEG数据集。该数据集包含来自30名参与者（12名女性，18名男性；年龄19.0-25.0岁）的64通道EEG记录（1000 Hz）和51通道fNIRS记录（47.62 Hz）。我们分析了EEG频谱功率和血流反应的时空特征，并验证了混合fNIRS-EEGBCIs在分类准确性方面的潜在优势。我们期望该数据集将促进神经成像分析和解码方法的评估和比较。
英文摘要：
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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