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.

翻译：单侧肢体运动想象在上肢运动康复与外部设备精确控制中具有重要作用，并对空间分辨率提出了更高要求。然而，现有公开数据集多关注基于粗略半球偏侧化的二分类或四分类左右肢体范式，仍缺乏同时记录单侧多方向运动想象脑电与功能近红外光谱信号的多模态数据集。为填补这一空白，我们构建了基于右上肢四类方向运动想象范式的公开数据集MIND。该数据集包含30名参与者（12名女性，18名男性；年龄19.0-25.0岁）的64通道脑电记录（1000 Hz）与51通道功能近红外光谱记录（47.62 Hz）。我们分析了脑电频谱功率与血流动力学响应的时空特征，验证了混合fNIRS-EEG脑机接口在分类准确率方面的潜在优势。期望本数据集能促进神经影像分析与解码方法的评估与比较。