Background: Following recent advances in technology, there is a growing interest in studying fatigue based on electrophysiological signals as a means of monitoring brain activity. While some existing works relate fatigue to performance, others consider the two as independent entities. Therefore, we must explore this intricate issue, particularly in laparoscopic training, for the sake of patient safety.
Objective: This paper explores and evaluates effects of fatigue on efficiency and accuracy based on laparoscopic surgical training using Electroencephalography (EEG) signal.
Materials and methods: 20 college students performed peg transfer task on laparoscopic simulator, with real-time recording of EEG signals for each subject. To monitor degree of fatigue, a real-time fatigue monitoring system based on fatigue analysis algorithm was designed through the use of EEG in alpha (α) and theta (θ) rhythms. We designed data acquisition and fatigue analysis modules based on MATLAB platform. BrainLink was used to record EEG signals and send them to personal computer wirelessly via Bluetooth. While artifacts from the captured EEG signals were removed using Blind Source Separation (BSS), α and θ rhythms were extracted using wavelet analysis. Fatigue was evaluated based on Regression Model and Mahalanobis Distance (DC ), and its threshold was determined from the experimental results using Receiver Operating Characteristic (ROC) curve analysis.
Results: Completion time and number of errors behaved like a decreasing function during the first few trials while increasing afterwards with the increasing of perceived fatigue level. The results indicate that learning curve of the subjects is increasing until 13th trials when they have attained maximum learning benefits and decreases afterwards due to fatigue.
Conclusion: Regression analysis shows that there are significant learning and fatigue effects when peg transfer task in the training is repeated in a series of trials. However, for the training to be effective and efficient, there should be monitoring during the training to observe where in the learning curve a trainee gains maximum learning benefits. Furthermore, fatigue is a significant indicator of efficiency and accuracy in terms of completion time and errors, respectively.