Laparoscopic simple nephrectomy (LSN) is an accepted treatment modality for nonfunctioning kidneys. Besides decreased postoperative morbidity, LSN is an advantage with decreased analgesic requirements and convalescence. LSN is a highly stressful operation, and the procedure requires high concentration level and experience. Emotions recognized from Electroencephalogram (EEG) may lead to detect the real emotions of the human. In this study, we proposed a subject-dependent stress level detection from EEG using the (Fpz beta/alpha) ratio to recognize high and low dominance levels of feelings based on the 2D Valence-Arousal model. The stress level of the surgeon is monitored via EEG during the operation. The most stressful phase of LSN and its change over time are determined using wireless EEG headset with real-time measurements. The aim here is to monitor and utilize objective information on the mental effort and stress demanded.