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Detection of Vigil and Fatigue States During Laparoscopic Tasks Based on EEG Patterns: Towards Neuroergonomics in Medical Training



Fatigue decreases performance in several professional activities. Fatigue can lead to commit technical mistakes, which consequences might be lethal, such as in health area, where a surgical error due to the absence of rest can provoke the patient death. Therefore, this study aims to detect vigil and fatigue (due to lack of sleep) states in medical students through the classification of electroencephalographic (EEG) patterns.


For this purpose, EEG signals of 18 physician students were analyzed within theta band (4–8 Hz) over fronto-central recording sites, and alpha band (8–13 Hz) rhythms over temporal and parieto-occipital recording sites during the execution of laparoscopic tasks before and after their medical duties at the Medical School of Tecnologico de Monterrey. EEG signal processing pipeline consisted in pre-processing based on independent component analysis, power spectral density estimates, and Support Vector Machine classification.


Resulting f-score to differ between vigil and fatigue states was 90.89%, where the first class was slightly more identifiable, reaching a sensitivity of 90.18%.


Based on this outcome, the detection of fatigue in medical students while their laparoscopic training seems achievable and feasible to diminish technical mistakes that could be lethal in health area. This project moves towards including neuro-ergonomics and human factors in medical trainings to improve the skill acquisition, and thus diminishing technical mistakes during surgeries.

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Data Availability

Preprocessed EEG signals of the 18 junior surgeons undertaking the five laparoscopic tasks can be downloaded from

Code Availability

Not applicable.


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The authors did not receive support from any organization for the submitted work.

Author information




YP elaborated the state-of-the-art, generated the discussion, translated the whole manuscript, and coordinated the team work. RB-G preprocessed the EEG signals, extracted the EEG feature, and undertook the first runs of the classification process. LMA-V proposed and supervised the EEG analysis, wrote the first version of the manuscript, and supervised and corrected the last version of the manuscript. DII-Z designed and implemented the presented classification process, and revised the manuscript. EAF-V, and CAR-G conducted the field investigation in the School of Medicine, and provided the EEG information.

Corresponding author

Correspondence to Luz María Alonso-Valerdi.

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All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Ethical Approval

The experimental procedure was formerly approved by the Ethical Committee of the National School of Medicine of the Tecnologico de Monterrey.

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All participants were informed about the experimental procedure and signed a consent form.

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Pérez, Y., Borboa-Gastelum, R., Alonso-Valerdi, L.M. et al. Detection of Vigil and Fatigue States During Laparoscopic Tasks Based on EEG Patterns: Towards Neuroergonomics in Medical Training. J. Med. Biol. Eng. (2021).

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  • Alpha
  • EEG Patterns
  • Laparoscopy
  • Fatigue
  • Theta
  • Vigil