| Despite advances in robotics, currently surgical robots are simple tools, controlled by surgeons. Also, surgeon-surgical robot interaction is a passive framework, while surgeons are responsible for performing different tasks of surgery in operating rooms. Adding intelligence to surgical robots demands understanding the process of motor command creation in a surgeon's brain and how this command changes while being transmitted to limbs and then to robot tools. Also, making operating rooms more dynamic and flexible demands monitoring the brain states as well as surgical skills of surgeons in real time. The introduction of such a surgical robot capable of actively collaborating with humans requires representing the process of motor-cognition surgical skills in the brain.;Since surgeons remotely control the robot in the closed-loop of human-robot interaction, the end-effector trajectory is corrected by perceiving the trajectory and transferences between hand and eye by practice during the learning process.;Human-robot interaction is a complicated skill that demands development of the cognition, perception, and motor skills during the learning process.;In this dissertation, by analyzing the brain dynamics as well as cognition development, using machine learning and graph theory, methods and algorithms are proposed for objective and dynamic performance monitoring and skill evaluation. Surgical skills are quantitatively evaluated using the proposed measurement parameters derived from observing brain reconfiguration during skill acquisition.;In addition, using brain functional state features, an objective trust evaluation algorithm is developed. The method is validated using clinical data, categorizing the impact of trust of mentors on fellows' performance. The developed method may be used to monitor the level of trust surgeons have in robot autonomous movements in the next generation of surgical robots (equipped with automation).;It is also hypothesized that dynamic reconfiguration of the brain results in more smooth and automatic hand movement while interacting with the robot. To investigate this hypothesis, extrinsic factors of robot tool trajectory, and intrinsic factors of brain activity are synchronized and analyzed together.;The results and findings throughout this research may also be applicable to therapy for the rehabilitation of stroke patients. Since hand movements in Robot-assisted Surgery are a combination of continuous complicated segments, results may also be used to investigate some unanswered questions in neuroscience, related to motor control. |
