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

• Published in: Human Mental Workload: Models and Applications pp. 105 - 117
• Main Authors: Di Flumeri, Gianluca, Aricò, Pietro, Borghini, Gianluca, Sciaraffa, Nicolina, Ronca, Vincenzo, Vozzi, Alessia, Storti, Silvia Francesca, Menegaz, Gloria, Fiorini, Paolo, Babiloni, Fabio
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2019
• Series: Communications in Computer and Information Science
• Subjects: EEG; Machine-learning; Neurometrics; Robot-assisted surgery; Workload
• ISBN: 9783030324223; 3030324222
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-030-32423-0_7

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.