Electroencephalogram and eye-gaze datasets for robot-assisted surgery performance evaluation

Surgical training and patient safety depend on objective and accurate
performance evaluation. Robot-assisted surgery (RAS) is newer and its training
less well defined, but resources are not easily accessed to support data
collection from participants with various RAS skill levels. This work
describes and makes available for public use electroencephalogram (EEG) and
eye-gaze data collected from 25 participants performing RAS simulator tasks
with varying levels of RAS experience.Twenty-seven tasks from six modules of
the _da Vinci ®;Skills Simulator _™, which are standard elements of most RAS
surgical skills training curricula, were taken into consideration. The dataset
contains performance scores (scale: 0 to 100) generated by the simulator after
each exercise was completed. Each task was completed by the participants at
least twice, and if they weren't able to complete it with a score of 70 (out
of 100) on at least one try, they tried again until they did. The National
Institute of Biomedical Imaging and Bioengineering (NIBIB) and Roswell Park
Comprehensive Cancer Center (RPCCC) supported the collection of data for use
in RAS training, which is why the dataset has been named the NIBIB-RPCCC-RAS
performance evaluation dataset. The dataset contains 1636 EEG recordings (in
the "*.edf" format), 1559 eye-gaze recordings (in the "*.csv" format),
performance scores, and participant demographic data, such as age, gender, and
dominant hand. To our knowledge, this is the first shared dataset that
contains EEG and eye-gaze data from participants with a variety of RAS
experiences who performed all tasks included in the da Vinci simulator
modules.