Presentation
Measuring Cognitive Load from EEG during Motor Control Calibration in Robotic-Assisted Surgery
Event Type
Poster
In-Person
TimeWednesday, October 6th5:00pm - 6:00pm EDT
LocationHarborside CDE
DescriptionRobotic-assisted surgery (RAS) systems are changing the way that surgeons are interacting with technology, particularly motion control of surgical instruments. Using traditional minimally invasive laparoscopic instruments cause inverted motions at the tooltip, while RAS interfaces are often more intuitive due to direct translation of movements. This difference in motion control leads to a calibration period between users who switch between the technology, and this period can lead to adjustments of users’ perception-action systems and increased cognitive load. The purpose of this pilot study was to measure brain activity through EEG signals during a calibration period and apply machine learning algorithms for prediction and feature selection. Simulated RAS tasks (n = 160) were performed with regular and inverted controls, and the first two minutes of the task was identified as the calibration period. It was found that EEG metrics associated with attentional demand and cognitive workload were higher during the inverted calibration period than non-calibration, and the decision tree model can predict calibration time with 85% accuracy.