Introduction: While Mental Workload (MWL) of surgeons during Robot-assisted surgery (RAS) has been frequently evaluated using subjective methods, currently there is no methodology proposed for an objective monitor. RAS is much more complicated than regular activities especially because it involves human-robot interaction, quick demands, appropriate reactions to uneven circumstances, and multitasking between surgical subtasks. In this study we used Electroencephalogram (EEG) to retrieve MW information while performing surgical tasks on a robot simulator.
Methods: EEG data from 22 medical students was recorded during six sessions over one year of practice, performing five RAS tasks on Robotic Surgery Simulator of robotic simulation curriculum - fundamental skills of robotic surgery (FSRS): Instrument Control, Ball Placement, Spatial Control II, Fourth Arm Tissue Retraction, and Hands-on Surgical Training (HoST) Anastomosis. EEG data was recorded using a 20-channel EEG headset (n= 420).
To calculate MWL, we used the framework developed by the B-Alert EEG series from Advanced Brain Monitoring Company- a frequently validated tool.
Network flexibility: Portion of time that brain area changes its functional community status as a respond toward processing a task. Utilizing network neuroscience and community detection techniques, network flexibility of 19 brain areas (one channel was corrupted and discarded) was extracted through each recording.
Results: We found significant correlation between network flexibility of most areas of the brain and the MWL value (Table 1).However, we didn’t find significant correlation between average flexibility over all areas of the brain and the MWL (0.03, p=0.56). These results show that different areas of the brain have different weights of involvement in total MWL. It is more proper to specify type of MWL as motor, cognitive, and perception MWL to be able to find the involvement of each brain area in specific type of mental workload.
Conclusions: Results of this study introduced brain regional network flexibility as a feature to be used for evaluation of motor, cognitive, and perceptual mental workloads. This novel method identifies level of mental workload types and can also be used to retrieve the reason of overloaded situations. Source of