Mental workload monitoring with multimodal approach during laparoscopic surgery training

The surgeon in laparoscopic surgery faces several challenges in terms of cognitive load and performance. As the mental workload of surgeons increases, the rates of surgical errors increase. Surgeons and surgical residents can be trained with laparoscopic surgery simulators using standard tasks. In this study, our aim is to investigate mental workload related biomarkers in the brain and human physiology with a multimodal approach. Due to the intricate nature of surgical skills and mental workload, it is essential to adopt multimodal approaches to investigate objective measures. In this thesis, behavioral and performance data such as NASA-TLX, completion time, and error rate were collected in addition to physiological methods such as ECG and EEG in surgeons and inexperienced subjects during laparoscopic tasks. Since surgical skills are mainly derived from brain activity, peripheral physiological parameters were also evaluated and investigated. The innovative aspect of this thesis is that these multimodal data will be used for assessment and training of surgeons.