2026-10-13T21:52:28Z https://www.scidb.cn/oai

10.57760/sciencedb.30795 2026-09-08T14:04:20Z

2026-09-08 SEFMEID: A Synchronous EEG-fNIRS Motor Execution and Imagery Dataset doi:10.57760/sciencedb.30795 en We introduce and openly release the Synchronous EEG-fNIRS Motor Execution and Imagery Dataset (SEFMEID), a concurrent hybrid EEG-fNIRS motor imagery dataset collected from 50 healthy participants. The dataset includes resting baseline, left-right hand motor execution (ME), and left-right hand motor imagery (MI) conditions, with each subject performing 25 trials for the right task and 25 trials for the left task. We also provide single-modality and multimodal classification results, as well as neurovascular coupling analyses, to validate the usability and quality of the dataset. In the deep learning results, the multimodal fusion algorithm achieves an improvement of approximately 10% over the best-performing unimodal approach. The public release of SEFMEID aims to serve as a valuable resource for advancing multimodal MI-BCI algorithms, investigating neurovascular coupling mechanisms underlying motor imagery, and accelerating clinical translation in neurological rehabilitation.  BCI; Motor Imagery; EEG-fNIRS; synchronous dataset Xuecheng Wu Guanchu Chen Wenhao Li Kai Li Jiyuan Xie Shibo Zhang Shuhao Chen Jiayi Zhou Wantong Qiu Kai Wu RESTRICTED dataset Science Data Bank