Abstract
Brain machine interfaces (BMIs) offer great potential to improve the quality of life for individuals with neurological disorders or severe motor impairments. Among various neural recording modalities, electroencephalogram (EEG) is particularly favorable for BMIs due to its noninvasive nature, portability, and high temporal resolution. Existing EEG datasets for BMIs are often limited to experimental settings that fail to address subjects’ freewill in decision making. We present a large EEG dataset, containing a total of 6808 trials, recorded from 23 healthy young adults (eight females and 15 males with an age range from 18 to 24 years) while performing reaching and grasping tasks, where the target object is freely chosen at their desired pace according to their own will. This EEG dataset provides a realistic representation of reaching and grasping movement, making it useful for developing practical BMIs.
Document Type
Article
Publication Date
2025
Digital Object Identifier (DOI)
https://doi.org/10.1038/s41597-025-06039-9
Funding Information
This work was partially supported by the College of Engineering Research Mini Grant Program and Dr. Jihye Bae’s Start Up Funds provided by the Department of Electrical and Computer Engineering at the University of Kentucky.
Repository Citation
Thapa, Bhoj Raj; Boggess, John; and Bae, Jihye, "A large electroencephalogram database of freewill reaching and grasping tasks for brain machine interfaces" (2025). Electrical and Computer Engineering Faculty Publications. 63.
https://uknowledge.uky.edu/ece_facpub/63
Notes/Citation Information
© The Author(s) 2025