Design and development of a framework to analyze performance and neural correlates of surgical learning

Surgical training often lack objective performance metrics, relying instead on instructor's subjective evaluations. With the advent of surgical simulators, quantitative performance data can be gathered. However, simulators often do not deliver real-time metrics but rather summarize the trainee's behavior after the exercise. Possible complementary measures are physiological signals. The goal of this study is to develop a framework to analyze physiological and behavioral data associated with surgical learning, and to define metrics to objectively evaluate surgical performance. We present a modular framework that allows for various training setups, such as physical or virtual laparoscopic trainers, or robotic-assisted surgical training, and different physical and physiological measurements, as movements and electroencephalography (EEG). The proposed system offers a broader perspective on surgical skill acquisition, allowing to track real-time cognitive and behavioral indices.