Development of performance and learning rate evaluation models in robot-assisted surgery using electroencephalography and eye-tracking.

ABSTRACT: The existing performance evaluation methods in robot-assisted surgery (RAS) are mainly subjective, costly, and affected by shortcomings such as the inconsistency of results and dependency on the raters' opinions. The aim of this study was to develop models for an objective evaluation of performance and rate of learning RAS skills while practicing surgical simulator tasks. The electroencephalogram (EEG) and eye-tracking data were recorded from 26 subjects while performing Tubes, Suture Sponge, and Dots and Needles tasks. Performance scores were generated by the simulator program. The functional brain networks were extracted using EEG data and coherence analysis. Then these networks, along with community detection analysis, facilitated the extraction of average search information and average

SUBMITTER: Shafiei SB 

PROVIDER: S-EPMC10799032 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature