Development of Proposed Algorithm for Neurometric Index (NI) Based on EEG Signals

Authors

  • A. Saidatul Biomedical Electronics Engineering, School of Mechatronics, Universiti Malaysia Perlis.
  • Vikneswaran Vijean Biomedical Electronics Engineering, School of Mechatronics, Universiti Malaysia Perlis.
  • M. Hariharan Department of Biomedical Engineering, SRM University, Kattankulathur Campus, Tamil Nadu, India

Keywords:

Alpha Asymmetry Score (AAS), Electroencephalography (EEG), Mental Stress Index, Stress Asymmetry Score (SAS),

Abstract

Nowadays, stress is one of the major issues where too much stress may lead to depression, fatigue and insomnia. Stress can be divided into two types called Eustress and Distress. Eustress or positive stress refers to the positive stress which helps to improve the performance of an individual. In contrast, Distress or negative stress can devastate a person by creating depression and damage the quality of life. It is essential to comprehend and to figure out the state of current stress in the numerical index. This study aims to find a new algorithm which can represent the mental stress condition in the numerical index. A new algorithm has been proposed based on the more established index, Alpha Asymmetry Score (AAS), as a reference. Modifications have been made in term of the frequency band as a variable in the stress index calculation. The classification accuracy of the proposed Stress Asymmetry Score (SAS) is approximately 96% which is 10% higher than AAS. SAS offers larger marginal relative difference at fast beta and slow alpha wave between the right and the left hemisphere, thus, it becomes the best discriminator for mental stress features in EEG classification. The development of the stress index promises a new era of stress brain-related research for future people’s benefit.

References

N. T. Sawyer and A. Escayg, “Stress and epilepsy: multiple models, multiple outcomes.,” J. Clin. Neurophysiol., vol. 27, no. 6, pp. 445–52, Dec. 2010.

R. P. Van de Kar LD, Richardson-Morton KD, “Stress:Neuroendocrine and pharmacological mechanism,” Methods Achieve Exp Pathol, vol. 14, pp. 133–173, 1991.

N. Weekes, R. Lewis, F. Patel, J. Garrison-Jakel, D. E. Berger, and S. J. Lupien, “Examination stress as an ecological inducer of cortisol and psychological responses to stress in undergraduate students.,” Stress, vol. 9, no. 4, pp. 199–206, Dec. 2006.

a. Kaklauskas et al., “Recommended Biometric Stress Management System,” Expert Syst. Appl., vol. 38, no. 11, pp. 14011–14025, May 2011.

P. Karthikeyan, M. Murugappan, and S. Yaacob, “A review on stress inducement stimuli for assessing human stress using physiological signals,” 2011 IEEE 7th Int. Colloq. Signal Process. its Appl., pp. 420– 425, Mar. 2011.

G. Krantz, M. Forsman, and U. Lundberg, “Consistency in physiological stress responses and electromyographic activity during induced stress exposure in women and men.,” Integr. Physiol. Behav. Sci., vol. 39, no. 2, pp. 105–18, 2004.

T. Yamakoshi et al., “A preliminary study on driver’s stress index using a new method based on differential skin temperature measurement.,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2007, pp. 722–5, Jan. 2007.

T. H. Lewis, R. S., Weekes, N. Y., & Wang, “The Effect of Naturalistic Stressor on Frontal Asymmetry, Stress and Health,” Biol. Psychol., vol. 75, pp. 239–247, 2007.

N. Hayatee et al., “Evaluation of Human Stress Using EEG Power Spectrum,” Methods, pp. 6–9, 2010.

R. S. Lewis, N. Y. Weekes, and T. H. Wang, “The effect of a naturalistic stressor on frontal EEG asymmetry, stress, and health.,” Biol. Psychol., vol. 75, no. 3, pp. 239–47, Jul. 2007.

N. Sulaiman et al., “Intelligent System for Assessing Human Stress Using EEG Signals and Psychoanalysis Tests,” 2011 Third Int. Conf. Comput. Intell. Commun. Syst. Networks, pp. 363–367, Jul. 2011.

K. N. S. Handri, S. Nomura, “Detecting stress based on the schedule of an intermittent mental workload using physiological sensor,” in Proceedings of the 11th International Conference on Humans and Computers, 2008, pp. 123–126.

R. K. Sinha, “EEG power spectrum and neural network based sleephypnogram analysis for a model of heat stress,” J. Clin. Monit. Comput., vol. 22, no. 4, pp. 261–268, 2008.

H. Hinrikus et al., “Electroencephalographic spectral asymmetry index for detection of depression.,” Med. Biol. Eng. Comput., vol. 47, no. 12, pp. 1291–9, Dec. 2009.

M. Teplan, “Fundamentals of EEG measurement,” Meas. Sci. Rev., vol. 2, no. 2, pp. 1–11, 2002.

A. Holm, K. Lukander, J. Korpela, M. Sallinen, and K. M. I. Müller, “Estimating Brain Load from the EEG,” ScientificWorldJournal., vol. 9, pp. 639–651, 2009.

Y. Tran, R. a. Thuraisingham, N. Wijesuriya, H. T. Nguyen, and a. Craig, “Detecting neural changes during stress and fatigue effectively: a comparison of spectral analysis and sample entropy,” 2007 3rd Int. IEEE/EMBS Conf. Neural Eng., pp. 350–353, May 2007.

R. L. Kelly and L. A. Wheaton, “Differential mechanisms of action understanding in left and right-handed subjects: The role of perspective and handedness,” Front. Psychol., vol. 4, no. DEC, 2013.

P. F. Lovibond, S.H. & Lovibond, Manual for the Depression Anxiety Stress Scales, 2nd edition. Sydney: Psychology Foundation, 1995.

J. L. Stewart, J. a Coan, D. N. Towers, and J. J. B. Allen, “Frontal EEG asymmetry during emotional challenge differentiates individuals with and without lifetime major depressive disorder.,” J. Affect. Disord., Sep. 2010.

K. Natarajan, R. A. U, F. Alias, T. Tiboleng, and S. K. Puthusserypady, “Nonlinear analysis of EEG signals at different mental states,” Biomed. Eng. Online, vol. 11, pp. 1–11, 2004.

Downloads

Published

2018-05-30

How to Cite

Saidatul, A., Vijean, V., & Hariharan, M. (2018). Development of Proposed Algorithm for Neurometric Index (NI) Based on EEG Signals. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-16), 71–74. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4097

Issue

Vol. 10 No. 1-16: Recent Trends in Biomedical Engineering Systems and Technologies I

Section

Articles

License

TRANSFER OF COPYRIGHT AGREEMENT

The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):

  1. All proprietary right other than copyright, such as patent rights
  2. The right to make further copies of all or part of the published article for my use in classroom teaching
  3. The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
  4. The right to make copies of the published work for internal distribution within the institution that employs me

I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.


Most read articles by the same author(s)