A multimodal approach combining tool-pressure and EEG features for laparoscopic skill classification using machine learning

Abstract

Aim: Laparoscopic skill assessment traditionally relies on subjective evaluation, which lacks objectivity and consistency. Automated multimodal approaches integrating tool-pressure and neural data may improve the reliability and scalability of skill assessment. Therefore, our objectives were to: (1) integrate a pressure-sensing unit into box-trainer simulators and laparoscopic tools to investigate tool-pressure features as objective indicators of surgical skill; and (2) combine electroencephalography (EEG)-derived power spectral density (PSD) and phase-locking value (PLV) features with tool-pressure data to evaluate the classification performance of different machine learning models.

Methods: Tool-pressure, EEG, and ECG data, along with task completion time, error counts, and National Aeronautics and Space Administration Task Load Index (NASA-TLX) workload scores, were collected from 10 surgeons and 13 inexperienced students performing a peg-transfer laparoscopic task. Pressure sensors were integrated into the right and left laparoscopic graspers. EEG features were extracted from four frequency bands using PSD and PLV. Three machine learning models - random forest classifier (RFC), Gaussian process classifier (GPC), and AdaBoost classifier (ABC) - were used to classify participants into surgeon and inexperienced groups.

Results: Right-left pressure asymmetry emerged as a reliable indicator of surgical expertise compared with other pressure metrics. Using only this feature, RFC achieved up to 78% classification accuracy. The highest performance occurred when combining theta-band power features with pressure asymmetry, where RFC and ABC reached 86% accuracy [F1 score = 0.83; area under the curve (AUC) = 0.92 for RFC].

Conclusion: This multimodal approach combining psychomotor and neurophysiological measures enhances the objectivity of surgical skill evaluation and may support real-time feedback systems for laparoscopic training.

Keywords

EEG / surgical skill / assessment / classification

References

[1] Omurtag A,Mansfield NJ.EEG connectivity and BDNF correlates of fast motor learning in laparoscopic surgery.Sci Rep2025;15:7399 PMCID:PMC11876304

[2] Bonrath EM,Zevin B.Defining technical errors in laparoscopic surgery: a systematic review.Surg Endosc2013;27:2678-91

[3] Buia A,Hanisch E.Laparoscopic surgery: a qualified systematic review.World J Methodol2015;5:238-54 PMCID:PMC4686422

[4] Birkmeyer JD, Finks JF, O’Reilly A, et al.; Michigan Bariatric Surgery Collaborative. Surgical skill and complication rates after bariatric surgery. N Engl J Med. 2013;369:1434-42.

[5] Keles HO,Demiral I,Omurtag A.High density optical neuroimaging predicts surgeons’s subjective experience and skill levels.PLoS ONE2021;16:e0247117 PMCID:7891714

[6] Healey MA,Osler TM,Burns E.Complications in surgical patients.Arch Surg2002;137:611-7

[7] Soangra R,Anirudh ER,John EB.Evaluation of surgical skill using machine learning with optimal wearable sensor locations.PLoS ONE2022;17:e0267936 PMCID:PMC9165861

[8] Shafiei SB,Mohler JL.Developing surgical skill level classification model using visual metrics and a gradient boosting algorithm.Ann Surg Open2023;4:e292 PMCID:10249659

[9] Zakeri Z,Sunderland C.Physiological correlates of cognitive load in laparoscopic surgery.Sci Rep2020;10:12927 PMCID:PMC7395129

[10] Darzi A,Mackay S.The challenge of objective assessment of surgical skill.Am J Surg2001;181:484-6

[11] Farah E,Marques C.Heart rate variability: an objective measure of mental stress in surgical simulation.Global Surg Educ2024;3:25

[12] The AF,van der Laan M.Heart rate variability as a measure of mental stress in surgery: a systematic review.Int Arch Occup Environ Health2020;93:805-21 PMCID:PMC7452878

[13] Huaulmé A,Thomazeau H.Automated assessment of non-technical skills by heart-rate data.Int J Comput Assist Radiol Surg2025;20:561-8

[14] Soangra R,Haik D.Beyond efficiency: surface electromyography enables further insights into the surgical movements of urologists.J Endourol2022;36:1355-61

[15] Soto Rodriguez NA,Porras Hernández JD.Objective evaluation of laparoscopic experience based on muscle electromyography and accelerometry performing circular pattern cutting tasks: a pilot study.Surg Innov2023;30:493-500

[16] Manabe T,Fu Y.Distinguishing laparoscopic surgery experts from novices using EEG topographic features.Brain Sci2023;13:1706 PMCID:PMC10742221

[17] Nemani A,Kruger U.Assessing bimanual motor skills with optical neuroimaging.Sci Adv2018;4:eaat3807 PMCID:PMC6170034

[18] Nemani A,Cooper CA,Intes X.Objective assessment of surgical skill transfer using non-invasive brain imaging.Surg Endosc2019;33:2485-94 PMCID:PMC10756643

[19] Gao Y,Kruger U.Functional brain imaging reliably predicts bimanual motor skill performance in a standardized surgical task.IEEE Trans Biomed Eng2021;68:2058-66 PMCID:8265734

[20] Zia A,Bettadapura V,Essa I.Video and accelerometer-based motion analysis for automated surgical skills assessment.Int J Comput Assist Radiol Surg2018;13:443-55

[21] Franco-gonzález IT,Bednarik R.Tracking 3D motion of instruments in microsurgery: a comparative study of stereoscopic marker-based vs. deep learning method for objective analysis of surgical skills.Inform Med Unlocked2024;51:101593

[22] Tien T,Sodergren MH,Yang GZ.Eye tracking for skills assessment and training: a systematic review.J Surg Res2014;191:169-78

[23] Oh J.Quantitative analysis of eye-gaze metrics in differentiating surgical expertise.Proc Hum Factors Ergon Soc Annu Meet2024;68:624-6

[24] Alleblas CC,Nieboer TE.Ergonomics of laparoscopic graspers and the importance of haptic feedback: the surgeons’ perspective.Gynecol Surg2016;13:379-84 PMCID:5133271

[25] Araki A,Yamanaka H.Comparison of the performance of experienced and novice surgeons: measurement of gripping force during laparoscopic surgery performed on pigs using forceps with pressure sensors.Surg Endosc2017;31:1999-2005

[26] Sugiyama T,Gan LS.Forces of tool-tissue interaction to assess surgical skill level.JAMA Surg2018;153:234-42 PMCID:PMC5885969

[27] Brown JD,Leung SC,Lee DI.Using contact forces and robot arm accelerations to automatically rate surgeon skill at peg transfer.IEEE Trans Biomed Eng2017;64:2263-75

[28] Rafii-Tari H,Bicknell C.Objective assessment of endovascular navigation skills with force sensing.Ann Biomed Eng2017;45:1315-27 PMCID:PMC5397443

[29] Golahmadi AK,Mylonas GP.Tool-tissue forces in surgery: a systematic review.Ann Med Surg2021;65:102268 PMCID:PMC8058906

[30] Dockter RL,Sweet RM.The minimally acceptable classification criterion for surgical skill: intent vectors and separability of raw motion data.Int J Comput Assist Radiol Surg2017;12:1151-9

[31] Gao Y,Intes X,De S.A machine learning approach to predict surgical learning curves.Surgery2020;167:321-7 PMCID:PMC6980926

[32] Ebina K, Abe T, Yan L, et al. Development of machine learning-based assessment system for laparoscopic surgical skills using motion-capture. In: Proceedings of the 2024 IEEE/SICE International Symposium on System Integration (SII); 2024 Jan 8-11; Ha Long, Vietnam. New York: IEEE; 2024. pp. 1-6.

[33] Power D,Madden MG.Automated assessment of simulated laparoscopic surgical skill performance using deep learning.Sci Rep2025;15:13591 PMCID:PMC12009314

[34] Natheir S,Yilmaz R.Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task.Comput Biol Med2023;152:106286

[35] Yin S.Multi-objective collaborative path planning for heterogeneous autonomous underwater vehicles in cluttered environments.Swarm Evol Comput2026;100:102251

[36] Yin S.Adaptive collision avoidance strategy for USVs in perception-limited environments using dynamic priority guidance.Adv Eng Inform2025;65:103355

[37] Omurtag A,Dehais F,Garbey M.Tracking mental workload by multimodal measurements in the operating room. Neuroergonomics. Elsevier; 2019. pp. 99-103.

[38] Muthukumaraswamy SD.High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations.Front Hum Neurosci2013;7:138 PMCID:PMC3625857

[39] Vinck M,van Wingerden M,Pennartz CM.An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias.Neuroimage2011;55:1548-65 PMCID:7920541

[40] Kim HJ,Park JS.Comparison of surgical skills in laparoscopic and robotic tasks between experienced surgeons and novices in laparoscopic surgery: an experimental study.Ann Coloproctol2014;30:71-6 PMCID:PMC4022755

[41] Dias RD,Boskovski MT,Yule SJ.Systematic review of measurement tools to assess surgeons’ intraoperative cognitive workload.Br J Surg2018;105:491-501 PMCID:5878696

[42] Hannah TC,Kellner R,Putrino D.Neuromonitoring correlates of expertise level in surgical performers: a systematic review.Front Hum Neurosci2022;16:705238 PMCID:PMC8888846

[43] Howie EE,Gunn E.Cognitive load management: an invaluable tool for safe and effective surgical training.J Surg Educ2023;80:311-22

[44] Balkhoyor AM,Biyani S.Frontal theta brain activity varies as a function of surgical experience and task error.BMJ Surg Interv Health Technol2020;2:e000040 PMCID:PMC8749254

[45] Akkad H,Kane E.Increasing human motor skill acquisition by driving theta-gamma coupling.Elife2021;10:e67355 PMCID:8687660

[46] Shafiei SB,Mohler JL.Surgical skill level classification model development using EEG and eye-gaze data and machine learning algorithms.J Robot Surg2023;17:2963-71 PMCID:PMC10678814