Abstract
The emergence of wearable low-cost wireless devices has allowed for continuous acquisition of physiological signals. Recently number of studies have applied these acquisition systems in different types of health monitoring. Since continuous elevation of stress hormones can have negative impact on individuals’ health, it is important to recognize and possibly prevent stress episodes in working environments. In this paper, we have tested if single-channel electroencephalography (EEG) signals can be utilized in assessment of different levels of mental workload and stress. Experimental study was conducted in laboratory settings with nine participants. In addition to EEG signals, we have acquired electrocardiogram (ECG) and electrodermal activity (EDA) recordings during all stages. Two scenarios are tested: first group of participants was introduced to only mental workload assignments, while second group was tested with mental workload and public speaking task as an stress inducing assignment. The experimental results show that EEG features have an acceptable separation ability between investigated states, where best classification accuracy, obtained between relaxed and high mental workload states, was 86.66%. Compared to only ECG or EDA features, EEG-based classification accuracy is higher in both scenarios, but lower in comparison with combined features from all three physiological signals.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baum, A. and Polsusnzy, D.: Health psychology: mapping biobehavioral contributions to health and illness, Annual Review of Psychology, vol. 50, pp. 137–163, 1999.
Alberdi, A., Aztiria, A. and Basarab, A.: Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review, Journal of biomedical informatics, 59, pp.49-75, 2016.
Healey, J.A. and Picard, R.W.: Detecting stress during real-world driving tasks using physiological sensors, IEEE Transactions on intelligent transportation systems, 6(2), pp.156-166, 2005.
Shi, Y., Nguyen, M.H., Blitz, P., French, B., Fisk, S., De la Torre, F., Smailagic, A., Siewiorek, D.P., al’Absi, M., Ertin, E. and Kamarck, T.: Personalized stress detection from physiological measurements, International symposium on quality of life technology (pp. 28-29), 2010.
Wang, S., Gwizdka, J. and Chaovalitwongse, W. A.: Using Wireless EEG Signals to Assess Memory Workload in the n-Back Task, IEEE Transactions on ON Human-Machine Systems, vol. 46, no. 3, 2016.
Zong, C. and Chetouani, M: Hilbert-Huang transform based physiological signals analysis for emotion recognition, IEEE International Symposium on Signal Processing and Information Technology, pp. 334-339, 2009.
Liu, Y., Sourina, O., and Nguyen, M. K.: Real-time EEG-based human emotion recognition and visualization, In, 2010 IEEE International Conference of Cyberworlds, pp. 262-269, 2010.
Zhang, H., Zhu, Y., Maniyeri, J. and Guan, C.: Detection of variations in cognitive workload using multi-modality physiological sensors and a large margin unbiased regression machine. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2985-2988, IEEE, 2014.
Hogervorst, M. A., Brouwer, A. M., and van Erp, J. B.: Combining and comparing EEG, peripheral physiology and eye-related measures for the assessment of mental workload, Frontiers in Neuroscience, vol.8, 322, 2014.
Saeed, S. M. U., Anwar S. M., Majid M. and Bhatti A. M.: Psychological stress measurement using low cost single channel EEG headset. IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 581–585 (2015)
Silva, H.P., Guerreiro, J., Loureno, A., Fred, A. and Martins, R.: BITalino: A Novel Hardware Framework for Physiological Computing. PhyCS, pp. 246-253. 2014.
Hardy, J. and Scanlon, M.: The science behind lumosity. San Francisco, CA: Lumos Labs (2009).
Spielberger, C.D.: Manual for the State-Trait Anxiety Inventory STAI (form Y)(" self-evaluation questionnaire"), 1983.
Chen, X., Liu, A., Peng, H. and Ward K. R.: A preliminary study of Muscular Arifact Cancellation in Single-Channel EEG, Sensors, vol. 14, pp. 18370-18389, 2014.
Wu, Z. and Huang, E. N.: Ensemble Empirical Mode Decomposition: A noise-assisted data analysis method. Advances in Adaptive Data Analysis, vol. 1, no. 1, pp.1–41, 2009.
Sweeney, K. T., McLoone, S. F., and Ward, T. E.: The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique, IEEE transactions on biomedical engineering, vol. 60, no. 1, pp. 97-105, 2013.
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Zheng, Q., Yen, N. C., Tung, C. C. and Liu, H.H.: The empirical mode decomposition and Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 454, no. 1971, pp. 903-995, 1998.
Pan J. and Tompkins W.J.; A real-time QRS detection algorithm, IEEE Transactions on Biomedical Engineering, vol. 3, pp. 230–236, 1985.
Peng, H., Long, F. and Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 8, pp.1226-1238, 2005.
Gevins, A., Smith, M. E., Leong, H., McEvoy, L., Whitfield, S., Du, R., and Rush, G.: Monitoring working memory load during computer-based tasks with EEG pattern recognition methods, Human Factors: The Journal of the Human Factors and Ergonomics Society, vol. 40, no. 1, pp. 79-91, 1998.
Rowland, N., Meile, M. J., and Nicolaidis, S.: EEG alpha activity reflects attentional demands, and beta activity reflects emotional and cognitive processes, Science, 228 (4700), pp. 750–752, 1985.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Secerbegovic, A., Ibric, S., Nisic, J., Suljanovic, N., Mujcic, A. (2017). Mental workload vs. stress differentiation using single-channel EEG. In: Badnjevic, A. (eds) CMBEBIH 2017. IFMBE Proceedings, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-10-4166-2_78
Download citation
DOI: https://doi.org/10.1007/978-981-10-4166-2_78
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4165-5
Online ISBN: 978-981-10-4166-2
eBook Packages: EngineeringEngineering (R0)