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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hao, G., Zhong, Y.: MentalWorkload assessment based on EEG and a hybrid ensemble classifier. Softw. Guide 18, 1–4 (2019)
Giorgos Giannakakis Member, IEEE, Dimitris Grigoriadis, Katerina Giannakaki, Olympia Simantiraki, Alexandros Roniotis and Manolis Tsiknakis, Member, IEEE: Review on psychological stress detection using biosignals
Salomao, T.; Alberto, L.: Operator functional state modelling and adaptive control of automation in human-machine systems. Ph.D. Thesis, University of Sheffield, Sheffield, UK (2016)
Hart, S.G., Staveland, L.E.: Development of NASA-TLX (task load index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183 (1988)
Reid, G.B., Nygren, T.E.: The subjective workload assessment technique: a scaling procedure for measuring mental workload. Adv. Psychol. 52, 185–218 (1988)
Li, Z.; Dong, Y.; Guo, X.: Hybrid brain-computer interface system based on alpha wave and motor imagery. J. Electron. Meas. Instrum. 28, 625–629 (2014).https://link.springer.com/article/10.1557/jmr.2014.218
Sornmo, L., Laguna, P.: Bioelectrical signal processing in cardiac and neurological application
Cacioppo, J., Tassinary, L.G., Berntson G.G.: The handbook of psychophysiology
Nyrio Ned. https://niryo.com/product/ned-education-research-cobot/
Enobio 20 Neuroelectrics. https://www.neuroelectrics.com/solutions/enobio/20
Neuroelectrics® Instrument Controller (NIC2). https://www.neuroelectrics.com/solution/software-integrations/nic2
Weka 3: Machine Learning Software in Java. https://www.cs.waikato.ac.nz/ml/weka/
Martinez, R., Salazar-Ramirez, A., Arruti, A., Irigoyen, E., Martin, J.I., Muguerza, J.: A self-paced relaxation response detection system based on Galvanic skin response analysis. https://ieeexplore.ieee.org/document/8680625
Electroencephalographic workload indicators during teleoperation of an unmanned aerial vehicle shepherding a swarm of unmanned ground vehicles in contested environments. https://www.frontiersin.org/articles/10.3389/fnins.2020.00040/full
Estimating brain load from the EEG. https://www.researchgate.net/publication/26681847_Estimating_Brain_Load_from_the_EEG
Int, J.: Environ. Res. Public Health (2020). https://doi.org/10.3390/ijerph17010046
Bujar, R., Longo, L.: An evaluation of the EEG alpha-to-theta and theta-to-alpha band ratios as indexes of mental workload. https://www.researchgate.net/publication/358919573_An_Evaluation_of_the_EEG_alpha-to-theta_and_theta-to-alpha_band_Ratios_as_Indexes_of_Mental_Workload
Github Repository. http://vislab.github.io/EEG-Blinks/
Acknowledgement
The authors would like to thank Marianna Turrà for providing support in experiment design and analyzing EDA signals.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-031-22731-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-22730-1
Online ISBN: 978-3-031-22731-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)