A Large Electroencephalogram Database of Freewill Reaching and Grasping Tasks for Brain Machine Interfaces

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.Users of this dataset are strongly encouraged to cite the accompanying Data Descriptor published in <i>Scientific Data</i>, which is listed in the <b>Related Materials</b> section of this Figshare dataset. The article provides detailed methodological information, data validation analyses, and usage notes essential for accurate interpretation of the recordings.