Continuous Monitoring of Mental Load During Virtual Simulator Training for Laparoscopic Surgery Reflects Laparoscopic Dexterity: A Comparative Study Using a Novel Wireless Device

Neta B Maimon, Maxim Bez, Denis Drobot, Lior Molcho, Nathan Intrator, Eli Kakiashvilli, Amitai Bickel, Neta B Maimon, Maxim Bez, Denis Drobot, Lior Molcho, Nathan Intrator, Eli Kakiashvilli, Amitai Bickel

Abstract

Introduction: Cognitive Load Theory (CLT) relates to the efficiency with which individuals manipulate the limited capacity of working memory load. Repeated training generally results in individual performance increase and cognitive load decrease, as measured by both behavioral and neuroimaging methods. One of the known biomarkers for cognitive load is frontal theta band, measured by an EEG. Simulation-based training is an effective tool for acquiring practical skills, specifically to train new surgeons in a controlled and hazard-free environment. Measuring the cognitive load of young surgeons undergoing such training can help to determine whether they are ready to take part in a real surgery. In this study, we measured the performance of medical students and interns in a surgery simulator, while their brain activity was monitored by a single-channel EEG.

Methods: A total of 38 medical students and interns were divided into three groups and underwent three experiments examining their behavioral performances. The participants were performing a task while being monitored by the Simbionix LAP MENTOR™. Their brain activity was simultaneously measured using a single-channel EEG with novel signal processing (Aurora by Neurosteer®). Each experiment included three trials of a simulator task performed with laparoscopic hands. The time retention between the tasks was different in each experiment, in order to examine changes in performance and cognitive load biomarkers that occurred during the task or as a result of nighttime sleep consolidation.

Results: The participants' behavioral performance improved with trial repetition in all three experiments. In Experiments 1 and 2, delta band and the novel VC9 biomarker (previously shown to correlate with cognitive load) exhibited a significant decrease in activity with trial repetition. Additionally, delta, VC9, and, to some extent, theta activity decreased with better individual performance.

Discussion: In correspondence with previous research, EEG markers delta, VC9, and theta (partially) decreased with lower cognitive load and higher performance; the novel biomarker, VC9, showed higher sensitivity to lower cognitive load levels. Together, these measurements may be used for the neuroimaging assessment of cognitive load while performing simulator laparoscopic tasks. This can potentially be expanded to evaluate the efficacy of different medical simulations to provide more efficient training to medical staff and measure cognitive and mental loads in real laparoscopic surgeries.

Keywords: EEG biomarker; brain assessment; cognitive load; laparoscopic operations; machine learning; mental load assessment; surgical simulator training.

Conflict of interest statement

NM is a part-time researcher at Neurosteer Ltd., which supplied the EEG system. LM is a clinical researcher at Neurosteer Ltd. NI is the founder of Neurosteer Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Maimon, Bez, Drobot, Molcho, Intrator, Kakiashvilli and Bickel.

Figures

FIGURE 1
FIGURE 1
A graphical representation of the study. (A) EEG data are recorded via a single channel using a three-electrode forehead patch by an Aurora EEG system while participants perform the simulator task. Data are pre-processed by a predefined set of wavelet packet atoms to produce 121 Brain Activity Features (BAFs). The BAFs are passed through linear models, which were pretrained on external data, to form a higher-level biomarker, VC9. Then, VC9 activity is averaged per trial and participant to find differences between simulator trials. (B) From the raw EEG data, power spectral density analysis is applied to find energy representation (in dB) produced for 1–50 Hz. The power density per Hz is averaged over frequency bands. Theta and delta activity are averaged per trial and participant to find differences between simulator trials. (C) Behavioral data are obtained from the Simbionix LAP MENTOR™ (i.e., time, accuracy, and economy of movement). These are averaged per trial and participant to find differences between simulator trials. Finally, correlations are calculated between EEG features (i.e., VC9, theta, and delta bands) and behavioral performance (time, accuracy, and economy of movement).
FIGURE 2
FIGURE 2
The means of accuracy (in percentage), economy of movement (in percentage), and time to exceed the task (in seconds) obtained in the three trials of Experiment 1, as a function of task repetitions: first trial (blue), second trial (red), and third trial (green), for all participants (n = 19). Error bars represent standard errors. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 3
FIGURE 3
(A) The distribution of participant VC9 activity (normalized between 1 and 100); (B) the distribution of theta (averaged power of 4–7 Hz in dBm); and (C) the distribution of delta (averaged power of 0.5–4 Hz in dBm), obtained in the three trials of Experiment 1, as a function of task repetitions: first trial (blue), second trial (red), and third trial (green), for all participants (n = 19). Dashed lines represent means and SDs. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 4
FIGURE 4
Mean activity of VC9 as a function of accuracy (A), economy (B), and time (C); theta as a function of accuracy (D), economy (E), and time (F); and delta as a function of accuracy (G), economy (H), and time (I) per trial and participant obtained in Experiment 1 (n = 19). Rrm and p-values presented.
FIGURE 5
FIGURE 5
The means of accuracy (in percentage), economy of movement (in percentage), and time to exceed the task (in seconds) obtained in the three trials of Experiment 2, as a function of task repetitions: first trial (blue), second trial (red), and third trial (green), for participants who underwent Experiment 2 (n = 10). Error bars represent standard errors. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 6
FIGURE 6
(A) The distribution of user VC9 activity (normalized between 1 and 100); (B) the distribution of theta (averaged power of 4–7 Hz in dBm); and (C) the distribution of delta (averaged power of 0.5–4 Hz in dBm), obtained in the three trials of Experiment 2, as a function of task repetitions: first trial (blue), second trial (red), and third trial (green), for all participants (n = 10). Dashed lines represent means and SDs. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 7
FIGURE 7
Mean activity of VC9 as function of accuracy (A), economy (B), and time (C); theta as function of accuracy (D), economy (E), and time (F); and delta as function of accuracy (G), economy (H), and time (I) per trial and participant obtained in Experiment 2 (n = 10). Rrm and p-values presented.
FIGURE 8
FIGURE 8
(A) The distribution of VC9 (normalized between 1 and 100), theta (averaged power of 4–7 Hz in dBm), and delta (averaged power of 0.5–4 Hz in dBm), obtained in the last trial repetition of Experiment 1 (blue), and the first task repetition of Experiment 2 (red), for participants who participated in both Experiment 1 and Experiment 2 (n = 10). Dashed lines represent means and SDs. (B) The means of accuracy (in percentage), economy of movement (in percentage), and time to exceed the task (in seconds), obtained in the last trial repetition of Experiment 1 (blue), and the first task repetition of Experiment 2 (red), for participants who participated in both Experiment 1 and Experiment 2 (n = 10). Error bars represent standard errors.
FIGURE 9
FIGURE 9
The means of accuracy (in percentage), economy of movement (in percentage), and time to exceed the task (in seconds) obtained in the three trials of Experiment 3, as a function of task repetitions: first trial (blue), second trial (red), and third trial (green), for participants who underwent Experiment 3 (n = 19). Error bars represent standard errors. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 10
FIGURE 10
(A) The distribution of user VC9 activity (normalized between 1 and 100); (B) the distribution of theta (averaged power of 4–7 Hz in dBm); and (C) the distribution of delta (averaged power of 0.5–4 Hz in dBm), obtained in the three trials of Experiment 3, as a function of task repetitions: first trial (blue), second trial (red), and third trial (green), for all participants (n = 19). Dashed lines represent means and SDs. *p < 0.05, **p < 0.01, and ***p < 0.001.
FIGURE 11
FIGURE 11
Mean activity of VC9 as a function of accuracy (A), economy (B), and time (C); theta as a function of accuracy (D), economy (E), and time (F); and delta as a function of accuracy (G), economy (H), and time (I) per trial and participant obtained in Experiment 3 (n = 19). Rrm and p-values presented.

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