PURPOSE: To measure gamma and alpha brain wave activity as a measurement of concentration and stress levels during surgical simulator performance of laparoscopic tasks to determine if expert surgeons have different brain activity patterns compared with intermediate and novice surgeons. MATERIALS AND METHODS: After obtaining Institutional Review Board approval, 1st and 2nd year medical students, urology residents (PGY2-PGY5), and attending urologists from one institution were recruited. Participants were stratified by level of experience and performed laparoscopic tasks on the EDGE laparoscopic simulator. Subjects were evaluated for concentration and stress levels using the electroencephalography (EEG) data extracted from the MUSE(™) headband. The MUSE software developer kit (SDK) allowed quantification of gamma and alpha waves during each task. An analysis of variance was used to compare concentration and stress levels between groups. RESULTS: A total of 19 participants were recruited for the study and stratified by surgical experience into novice, intermediate, and expert laparoscopy groups: 6 medical students, 9 urology residents, and 4 attending urologists, respectively. Concentration and stress were quantified by calculating the area under the curve of the gamma and alpha EEG wave tracings. Stress was significantly lower in the attending urologists compared with the residents and medical students during the laparoscopic suturing and trended toward significance in the peg transfer task (P = 0.0003, P = 0.069). Concentration was significantly higher in the expert group compared with the less experienced groups during both the peg and suture tasks (P = 0.036, P = 0.0039). CONCLUSIONS: EEG brain activity in more experienced surgeons reveals a significant increase in concentration levels with a decrease in stress during simulated laparoscopic tasks compared with novices. This information may correlate with increased proficiency as well as provide objective feedback of progress along the learning curve with the MUSE SDK.
PURPOSE: To measure gamma and alpha brain wave activity as a measurement of concentration and stress levels during surgical simulator performance of laparoscopic tasks to determine if expert surgeons have different brain activity patterns compared with intermediate and novice surgeons. MATERIALS AND METHODS: After obtaining Institutional Review Board approval, 1st and 2nd year medical students, urology residents (PGY2-PGY5), and attending urologists from one institution were recruited. Participants were stratified by level of experience and performed laparoscopic tasks on the EDGE laparoscopic simulator. Subjects were evaluated for concentration and stress levels using the electroencephalography (EEG) data extracted from the MUSE(™) headband. The MUSE software developer kit (SDK) allowed quantification of gamma and alpha waves during each task. An analysis of variance was used to compare concentration and stress levels between groups. RESULTS: A total of 19 participants were recruited for the study and stratified by surgical experience into novice, intermediate, and expert laparoscopy groups: 6 medical students, 9 urology residents, and 4 attending urologists, respectively. Concentration and stress were quantified by calculating the area under the curve of the gamma and alpha EEG wave tracings. Stress was significantly lower in the attending urologists compared with the residents and medical students during the laparoscopic suturing and trended toward significance in the peg transfer task (P = 0.0003, P = 0.069). Concentration was significantly higher in the expert group compared with the less experienced groups during both the peg and suture tasks (P = 0.036, P = 0.0039). CONCLUSIONS: EEG brain activity in more experienced surgeons reveals a significant increase in concentration levels with a decrease in stress during simulated laparoscopic tasks compared with novices. This information may correlate with increased proficiency as well as provide objective feedback of progress along the learning curve with the MUSE SDK.