Abstract
The present study aims to investigate the effect of human-robot collaboration on brain activity. To this end, we analyze differences in the electroencephalogram (EEG) power spectrum (Theta, Alpha and Beta frequency bands), and state-of-the-art indices, between some participants who performed different experiments, including a experiment of human-robot collaboration. In particular, tests included low cognitive load tasks, such as listening to classical music and watching a relaxing video; tasks with medium cognitive load through a collaborative robotics experiment, developed using the Niryo Ned robot, structured in three subtasks with increasing difficulty; and a high cognitive load task consisting of a sudoku game with tight time constraint. The EEG was recorded using the Neuroelectrics Enobio20 helmet. In addition to recording EEG signals, electrodermal activity (EDA) was recorded by means of a BITalino (R)Evolution Board in order to compare the results obtained from the two biosignals and evaluate the indices deriving from the spectral powers of the rhythms. Most of the workload indices, present in the literature, used for the analysis of this work have proved, on the basis of the analysis carried out, to be very good indices of the cognitive load.
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Acknowledgement
The authors would like to thank Marianna Turrà for providing support in experiment design and analyzing EDA signals.
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Ruo, A., Villani, V., Sabattini, L. (2023). Use of EEG Signals for Mental Workload Assessment in Human-Robot Collaboration. In: Borja, P., Della Santina, C., Peternel, L., Torta, E. (eds) Human-Friendly Robotics 2022. HFR 2022. Springer Proceedings in Advanced Robotics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-031-22731-8_17
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