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EEG-Based Workload Index as a Taxonomic Tool to Evaluate the Similarity of Different Robot-Assisted Surgery Systems

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Human Mental Workload: Models and Applications (H-WORKLOAD 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1107))

Abstract

In operational fields, there is a growing use of simulators during training protocols because of their versatility, the possibility of limiting costs and increasing efficiency. This work aimed at proposing an EEG-based neurometric of mental workload, previously validated in other contexts, as a taxonomic tool to evaluate the similarity, in terms of cognitive demands, of two different systems: the da Vinci surgical system, leader in the field of robotic surgery, and the Actaeon Console by BBZ, basically a cheaper simulator aimed to train students to use the da Vinci system. Such a neurophysiologic evaluation of the workload demand was also integrated by information derived by the task performance and self-reports. The results validated the proposed EEG-based workload index and indicated the potentially fruitful use of simulators because of their high similarity in terms of cognitive demands.

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Acknowledgements

The BBZ team and the University of Verona are sincerely acknowledged for allowing the use of their facilities.

Author information

Authors and Affiliations

  1. Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy

    Gianluca Di Flumeri, Pietro Aricò, Gianluca Borghini & Fabio Babiloni

  2. Department of Anatomical, Histological, Forensic and Orthopedic Sciences, Sapienza University of Rome, Rome, Italy

    Nicolina Sciaraffa

  3. BrainSigns Srl, Rome, Italy

    Gianluca Di Flumeri, Pietro Aricò, Gianluca Borghini, Nicolina Sciaraffa, Vincenzo Ronca, Alessia Vozzi & Fabio Babiloni

  4. Department of Computer Science, Hangzhou Dianzi University, Hangzhou, China

    Fabio Babiloni

  5. Department of Computer Science, University of Verona, Verona, Italy

    Silvia Francesca Storti, Gloria Menegaz & Paolo Fiorini

Authors
  1. Gianluca Di Flumeri
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  2. Pietro Aricò
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  3. Gianluca Borghini
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  4. Nicolina Sciaraffa
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  5. Vincenzo Ronca
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  6. Alessia Vozzi
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  7. Silvia Francesca Storti
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  8. Gloria Menegaz
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  9. Paolo Fiorini
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  10. Fabio Babiloni
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Corresponding author

Correspondence to Gianluca Di Flumeri .

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Editors and Affiliations

  1. Technological University Dublin, Dublin, Ireland

    Luca Longo

  2. Technological University Dublin, Dublin, Ireland

    Maria Chiara Leva

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Di Flumeri, G. et al. (2019). EEG-Based Workload Index as a Taxonomic Tool to Evaluate the Similarity of Different Robot-Assisted Surgery Systems. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2019. Communications in Computer and Information Science, vol 1107. Springer, Cham. https://doi.org/10.1007/978-3-030-32423-0_7

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  • DOI: https://doi.org/10.1007/978-3-030-32423-0_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32422-3

  • Online ISBN: 978-3-030-32423-0

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