EEG-BCI Dataset for Real-time Robotic Hand Control at Individual Finger Level

In this study, subjects controlled an EEG-based BCI using motor execution or motor imagery of their fingers within the dominant hand to control the corresponding finger motions of a robotic hand in real time. Twenty-one right-handed subjects participated in one offline and two online sessions each for finger motor execution and motor imagery tasks. Sixteen out of the twenty-one subjects completed three more motor imagery online sessions and two more online sessions with smoothed robotic control, one for finger motor execution tasks and one for finger motor imagery tasks. The dataset includes EEG recordings and real-time finger decoding results for each subject during multiple sessions. The detailed description of the study can be found in the following publication:

Ding, Y., Udompanyawit, C., Zhang, Y., & He, B. (2025). EEG-based brain-computer interface enables real-time robotic hand control at individual finger level. Nature communications, 16(1), 5401. https://doi.org/10.1038/s41467-025-61064-x

If you use a part of this dataset in your work, please cite the above publication.

This dataset was collected under support from the National Institutes of Health via grants NS124564, NS131069, NS127849, and NS096761 to Dr. Bin He.

Correspondence about the dataset: Dr. Bin He, Carnegie Mellon University, Department of Biomedical Engineering, Pittsburgh, PA 15213. E-mail: bhe1@andrew.cmu.edu