{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T12:12:20Z","timestamp":1776082340448,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Health Institutes of T\u00fcrkiye","award":["2023-B-01-33153"],"award-info":[{"award-number":["2023-B-01-33153"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gentleness, defined as the ability to handle tissues delicately while minimizing unnecessary force, is a critical indicator of surgical proficiency. Objective and real-time assessment of gentleness in virtual reality (VR)-based training can improve the understanding of both psychomotor and cognitive components of surgical skill. This study evaluates and classifies participants\u2019 gentleness during VR-based laparoscopic simulations using fNIRS-derived hemodynamic features. Twenty-three volunteers with no prior laparoscopic experience performed a VR-based double-grasper task while hemodynamic activity over frontal and motor cortical regions was recorded using eighteen fNIRS channels. In parallel, subjective workload (NASA-TLX), error counts, and gentleness performance score (GPS) were collected. Temporal features, including slope, root mean square, and standard deviation, were extracted from the fNIRS signals and used to train multiple machine learning classifiers. Performance labels were binarized into low and high groups using median splits of the gentleness performance score. Models were evaluated using stratified 5-fold cross-validation. Results revealed stronger right-frontal HbO activity and increased left-motor HbR responses in the low-performance group, suggesting higher cognitive effort and less efficient motor strategies. Across classifiers, slope-based features consistently outperformed variability- and amplitude-based metrics. The highest classification performance was achieved using HbR slope features with Random Forest classifiers (accuracy \u2248 0.85, AUC up to 0.93). These findings highlight the potential of fNIRS-based metrics for automated performance assessment in VR surgical training.<\/jats:p>","DOI":"10.3390\/s26082388","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:38:26Z","timestamp":1776080306000},"page":"2388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating and Classifying Gentleness in VR-Based Surgical Simulation: A VR + fNIRS Study"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-0705-8189","authenticated-orcid":false,"given":"Suveyda","family":"Sanli","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Ankara University, Ankara 06560, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8493-2582","authenticated-orcid":false,"given":"Hasan Onur","family":"Keles","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Ankara University, Ankara 06560, Turkey"},{"name":"Brain Research Center (AUBAUM), Ankara University, Ankara 06900, Turkey"},{"name":"Neuroscience and Neurotechnology Center of Excellence Brain Research Center (NOROM), Ankara University, Ankara 06560, Turkey"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.7861\/fhj.2019-0036","article-title":"Virtual Reality and the Transformation of Medical Education","volume":"6","author":"Pottle","year":"2019","journal-title":"Future Healthc. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0010-4825(94)00009-F","article-title":"Virtual Reality in Surgical Education","volume":"25","author":"Ota","year":"1995","journal-title":"Comput. Biol. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yu, P., Pan, J., Wang, Z., Shen, Y., Li, J., Hao, A., and Wang, H. (2022). Quantitative Influence and Performance Analysis of Virtual Reality Laparoscopic Surgical Training System. BMC Med. Educ., 22.","DOI":"10.1186\/s12909-022-03150-y"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.jss.2021.06.045","article-title":"de Immersive Virtual Reality for Surgical Training: A Systematic Review","volume":"268","author":"Mao","year":"2021","journal-title":"J. Surg. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.neuroscience.2024.12.040","article-title":"Virtual Reality Modulating Dynamics of Neuroplasticity: Innovations in Neuro-Motor Rehabilitation","volume":"566","author":"Wankhede","year":"2025","journal-title":"Neuroscience"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e24526","DOI":"10.2196\/24526","article-title":"Effect of Cognitive Training in Fully Immersive Virtual Reality on Visuospatial Function and Frontal-Occipital Functional Connectivity in Predementia: Randomized Controlled Trial","volume":"23","author":"Kang","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102945","DOI":"10.1016\/j.amsu.2021.102945","article-title":"Virtual Reality (VR) as a Simulation Modality for Technical Skills Acquisition","volume":"71","author":"Nassar","year":"2021","journal-title":"Ann. Med. Surg."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s41539-022-00138-7","article-title":"Directed Information Flow during Laparoscopic Surgical Skill Acquisition Dissociated Skill Level and Medical Simulation Technology","volume":"7","author":"Kamat","year":"2022","journal-title":"Npj Sci. Learn."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Aksoy, E., Izzetoglu, K., Baysoy, E., Agrali, A., Kitapcioglu, D., and Onaral, B. (2019). Performance Monitoring via Functional Near Infrared Spectroscopy for Virtual Reality Based Basic Life Support Training. Front. Neurosci., 13.","DOI":"10.3389\/fnins.2019.01336"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tremmel, C., Herff, C., Sato, T., Rechowicz, K., Yamani, Y., and Krusienski, D.J. (2019). Estimating Cognitive Workload in an Interactive Virtual Reality Environment Using EEG. Front. Hum. Neurosci., 13.","DOI":"10.3389\/fnhum.2019.00401"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e2105","DOI":"10.1002\/rcs.2105","article-title":"Systematic Approach for Content and Construct Validation: Case Studies for Arthroscopy and Laparoscopy","volume":"16","author":"Farmer","year":"2020","journal-title":"Int. J. Med. Robot. Comput. Assist. Surg."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1007\/s00464-007-9499-y","article-title":"Effects of Experience on Force Perception Threshold in Minimally Invasive Surgery","volume":"22","author":"Zhou","year":"2008","journal-title":"Surg. Endosc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1002\/bjs.9669","article-title":"Influence of Visual Force Feedback on Tissue Handling in Minimally Invasive Surgery","volume":"101","author":"Rodrigues","year":"2014","journal-title":"Br. J. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1177\/00187208241263684","article-title":"Sensorimotor Challenges in Minimally Invasive Surgery: A Theoretically-Oriented Review","volume":"67","author":"Hewitson","year":"2025","journal-title":"Hum. Factors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1056\/NEJMsa1300625","article-title":"Surgical Skill and Complication Rates after Bariatric Surgery","volume":"369","author":"Birkmeyer","year":"2025","journal-title":"N. Engl. J. Med."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Keles, H.O., Cengiz, C., Demiral, I., Ozmen, M.M., and Omurtag, A. (2021). High Density Optical Neuroimaging Predicts Surgeons\u2019s Subjective Experience and Skill Levels. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0247117"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"12927","DOI":"10.1038\/s41598-020-69553-3","article-title":"Physiological Correlates of Cognitive Load in Laparoscopic Surgery","volume":"10","author":"Zakeri","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7399","DOI":"10.1038\/s41598-025-89261-0","article-title":"EEG Connectivity and BDNF Correlates of Fast Motor Learning in Laparoscopic Surgery","volume":"15","author":"Omurtag","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"109035","DOI":"10.1016\/j.surg.2024.109035","article-title":"Surgical Simulation Educational Research","volume":"180","author":"Nestel","year":"2025","journal-title":"Surgery"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2106","DOI":"10.1007\/s00464-014-3442-9","article-title":"Development of Force-Based Metrics for Skills Assessment in Minimally Invasive Surgery","volume":"28","author":"Trejos","year":"2014","journal-title":"Surg. Endosc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"307.e1","DOI":"10.1016\/j.ajog.2023.05.007","article-title":"Output Force and Ratio of Laparoscopic Graspers: An Evaluation of Operating Room Ergonomics","volume":"229","author":"Olig","year":"2023","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_22","unstructured":"Ayaz, H., and Dehais, F. (2019). Chapter 16\u2014Tracking Mental Workload by Multimodal Measurements in the Operating Room. Neuroergonomics, Academic Press."},{"key":"ref_23","first-page":"430","article-title":"Multimodal Approach to Assess a Virtual Reality-Based Surgical Training Platform","volume":"Volume 14027","author":"Demirel","year":"2023","journal-title":"Virtual, Augmented and Mixed Reality, Proceedings of the 25th HCI International Conference, HCII 2023, Copenhagen, Denmark, 23\u201328 July 2023"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"683","DOI":"10.1097\/SLA.0000000000002289","article-title":"Temporal Stress in the Operating Room: Brain Engagement Promotes \u201cCoping\u201d and Disengagement Prompts \u201cChoking\u201d","volume":"267","author":"Modi","year":"2018","journal-title":"Ann. Surg."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"95","DOI":"10.47717\/turkjsurg.2023.5674","article-title":"Video Game Experience Affects Performance, Cognitive Load, and Brain Activity in Laparoscopic Surgery Training","volume":"39","author":"Omurtag","year":"2023","journal-title":"Turkish J. Surg."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1007\/s00464-018-6535-z","article-title":"Objective Assessment of Surgical Skill Transfer Using Non-Invasive Brain Imaging","volume":"33","author":"Nemani","year":"2019","journal-title":"Surg. Endosc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2058","DOI":"10.1109\/TBME.2020.3014299","article-title":"Functional Brain Imaging Reliably Predicts Bimanual Motor Skill Performance in a Standardized Surgical Task","volume":"68","author":"Gao","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nemani, A., Intes, X., and De, S. (2015, January 17\u201319). Surgical Motor Skill Differentiation via Functional near Infrared Spectroscopy. Proceedings of the 2015 41st Annual Northeast Biomedical Engineering Conference (NEBEC), Troy, NY, USA.","DOI":"10.1109\/NEBEC.2015.7117212"},{"key":"ref_29","first-page":"e292","article-title":"Developing Surgical Skill Level Classification Model Using Visual Metrics and a Gradient Boosting Algorithm","volume":"4","author":"Shafiei","year":"2023","journal-title":"Ann. Surg. Open Perspect. Surg. Hist. Educ. Clin. Approaches"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"eaat3807","DOI":"10.1126\/sciadv.aat3807","article-title":"Assessing Bimanual Motor Skills with Optical Neuroimaging","volume":"4","author":"Nemani","year":"2018","journal-title":"Sci. Adv."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3004","DOI":"10.1007\/s00464-024-10830-x","article-title":"Using Neuroimaging to Assess Brain Activity and Areas Associated with Surgical Skills: A Systematic Review","volume":"38","author":"Andersen","year":"2024","journal-title":"Surg. Endosc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e198363","DOI":"10.1001\/jamanetworkopen.2019.8363","article-title":"Machine Learning Identification of Surgical and Operative Factors Associated With Surgical Expertise in Virtual Reality Simulation","volume":"2","author":"Yilmaz","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Prevezanou, K., Seimenis, I., Karaiskos, P., Pikoulis, E., Lykoudis, P.M., and Loukas, C. (2024). Machine Learning Approaches for Evaluating the Progress of Surgical Training on a Virtual Reality Simulator. Appl. Sci., 14.","DOI":"10.3390\/app14219677"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5197","DOI":"10.1038\/s41598-021-84295-6","article-title":"Automation of Surgical Skill Assessment Using a Three-Stage Machine Learning Algorithm","volume":"11","author":"Lavanchy","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1088\/0967-3334\/33\/2\/259","article-title":"Wavelet-Based Motion Artifact Removal for Functional near-Infrared Spectroscopy","volume":"33","author":"Molavi","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1364\/AO.36.000021","article-title":"Theoretical and Experimental Investigation of Near-Infrared Light Propagation in a Model of the Adult Head","volume":"36","author":"Okada","year":"1997","journal-title":"Appl. Opt."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Naseer, N., and Hong, K.-S. (2015). FNIRS-Based Brain-Computer Interfaces: A Review. Front. Hum. Neurosci., 9.","DOI":"10.3389\/fnhum.2015.00003"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1007\/s00268-009-0151-y","article-title":"Systematic Video Game Training in Surgical Novices Improves Performance in Virtual Reality Endoscopic Surgical Simulators: A Prospective Randomized Study","volume":"33","author":"Schlickum","year":"2009","journal-title":"World J. Surg."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"e43109","DOI":"10.1016\/j.heliyon.2025.e43109","article-title":"Influence of Video Gaming Experience on Motor Adaptation during Visuomotor and Force Field Perturbations","volume":"11","author":"Kunavar","year":"2025","journal-title":"Heliyon"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2769","DOI":"10.1007\/s00221-020-05931-7","article-title":"Influence of Action Video Gaming on Spatial Representation in the Haptic Modality","volume":"238","author":"Jiao","year":"2020","journal-title":"Exp. Brain Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e194108","DOI":"10.1001\/jamanetworkopen.2019.4108","article-title":"Efficacy of Mindfulness-Based Cognitive Training in Surgery: Additional Analysis of the Mindful Surgeon Pilot Randomized Clinical Trial","volume":"2","author":"Lebares","year":"2019","journal-title":"JAMA Netw. Open"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.jsurg.2012.06.006","article-title":"Comparing Video Games and Laparoscopic Simulators in the Development of Laparoscopic Skills in Surgical Residents","volume":"69","author":"Adams","year":"2012","journal-title":"J. Surg. Educ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Brucker, B., Pardi, G., Uehlin, F., Moosmann, L., Lachmair, M., Halfmann, M., and Gerjets, P. (2024). How Learners\u2019 Visuospatial Ability and Different Ways of Changing the Perspective Influence Learning About Movements in Desktop and Immersive Virtual Reality Environments, Springer.","DOI":"10.1007\/s10648-024-09895-w"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"208","DOI":"10.3758\/s13423-012-0351-6","article-title":"Neural Evidence Supports a Novel Framework for Spatial Navigation","volume":"20","author":"Chrastil","year":"2013","journal-title":"Psychon. Bull. Rev."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e3383","DOI":"10.1002\/brb3.3383","article-title":"Neural Correlates of Fine Motor Grasping Skills: Longitudinal Insights into Motor Cortex Activation Using FNIRS","volume":"14","author":"Li","year":"2024","journal-title":"Brain Behav."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e69977","DOI":"10.7554\/eLife.69977","article-title":"Left Hemisphere Dominance for Bilateral Kinematic Encoding in the Human Brain","volume":"11","author":"Merrick","year":"2022","journal-title":"elife"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Hannah, T.C., Turner, D., Kellner, R., Bederson, J., Putrino, D., and Kellner, C.P. (2022). Neuromonitoring Correlates of Expertise Level in Surgical Performers: A Systematic Review. Front. Hum. Neurosci., 16.","DOI":"10.3389\/fnhum.2022.705238"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Modi, H.N., Osborne-Grinter, M., Patel, R., Darzi, A., Leff, D.R., and Singh, H. (2024). Investigating the Impact of Mental Rehearsal on Prefrontal and Motor Cortical Haemodynamic Responses in Surgeons Using Optical Neuroimaging. Front. Hum. Neurosci., 18.","DOI":"10.3389\/fnhum.2024.1386005"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"31405","DOI":"10.1117\/1.NPh.3.3.031405","article-title":"False Positives and False Negatives in Functional Near-Infrared Spectroscopy: Issues, Challenges, and the Way Forward","volume":"3","author":"Tachtsidis","year":"2016","journal-title":"Neurophotonics"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Herff, C., Heger, D., Fortmann, O., Hennrich, J., Putze, F., and Schultz, T. (2013). Mental Workload during N-Back Task-Quantified in the Prefrontal Cortex Using FNIRS. Front. Hum. Neurosci., 7.","DOI":"10.3389\/fnhum.2013.00935"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2388\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:39:29Z","timestamp":1776080369000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/8\/2388"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":50,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26082388"],"URL":"https:\/\/doi.org\/10.3390\/s26082388","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,13]]}}}