Robotics-Assisted Surgical Skills Evaluation based on Electrocortical Activity.

Area of Science:

• Robotics
• Neuroscience
• Surgical Education

Background:

• Current Robotics-Assisted Minimally Invasive Surgery (RAMIS) skills assessment relies on temporal, motion, and outcome metrics.
• These metrics do not fully capture all aspects of skilled surgical performance, particularly cognitive states like stress and attention.
• Electrocortical activity's impact on RAMIS surgeon performance is understudied.

Purpose of the Study:

• To investigate novel performance metrics based on electroencephalography (EEG) signals for RAMIS skills assessment.
• To explore the potential augmentation of RAMIS training and assessment platforms with EEG-based metrics.
• To analyze differences in electrocortical activity between novice and expert RAMIS surgeons during simulated tasks.

Main Methods:

• A user study involved 23 novice and 9 expert surgeons performing tasks on a RAMIS simulator (dv-Trainer®).
• Electroencephalography (EEG) signals were recorded using a Muse EEG headband.
• Performance metrics were derived from mean band powers of EEG signals across different frequency ranges and electrode locations.

Main Results:

• Statistically significant differences in electrocortical activity were found between novices and experts in temporoparietal and left frontal regions.
• These differences were observed in mid to high-frequency ranges of EEG signals.
• Expert surgeons exhibited lower levels of electrocortical activity in these regions compared to novices.

Conclusions:

• Electrocortical activity, measured via EEG, shows potential for augmenting RAMIS skills assessment.
• EEG-based metrics can provide valuable insights into the cognitive states of surgeons during RAMIS procedures.
• This research paves the way for more comprehensive skills evaluation in surgical robotics training.