Association between Functional Brain Network Metrics and Surgeon Performance and Distraction in the Operating Room

• Main Authors: Somayeh B. Shafiei, Zhe Jing, Kristopher Attwood, Umar Iqbal, Sena Arman, Ahmed A. Hussein, Mohammad Durrani, Khurshid Guru
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Brain Sciences
• Subjects: robot-assisted surgery; electroencephalogram; functional brain network; RAS surgical performance
• Online Access: https://www.mdpi.com/2076-3425/11/4/468

Objective: The aim of this work was to examine (electroencephalogram) EEG features that represent dynamic changes in the functional brain network of a surgical trainee and whether these features can be used to evaluate a robot assisted surgeon’s (RAS) performance and distraction level in the operating room. Materials and Methods: Electroencephalogram (EEG) data were collected from three robotic surgeons in an operating room (OR) via a 128-channel EEG headset with a frequency of 500 samples/second. Signal processing and network neuroscience algorithms were applied to the data to extract EEG features. The SURG-TLX and NASA-TLX metrics were subjectively evaluated by a surgeon and mentor at the end of each task. The scores given to performance and distraction metrics were used in the analyses here. Statistical test data were utilized to select EEG features that have a significant relationship with surgeon performance and distraction while carrying out a RAS surgical task in the OR. Results: RAS surgeon performance and distraction had a relationship with the surgeon’s functional brain network metrics as recorded throughout OR surgery. We also found a significant negative Pearson correlation between performance and the distraction level (−0.37, <i>p</i>-value < 0.0001). Conclusions: The method proposed in this study has potential for evaluating RAS surgeon performance and the level of distraction. This has possible applications in improving patient safety, surgical mentorship, and training.