Google Scholar

##plugins.themes.academic_pro.article.sidebar##

Published
Sep 4, 2014
Download
PDF
Statistic
Read Counter : 40 Download : 28

Downloads

Download data is not yet available.

##plugins.themes.academic_pro.article.main##

Abstract

Abstract:

Nonlinear analysis of electroencephalogram (EEG) activity can provide a better understanding of brain signal dynamics during cognitive fatigue. The aim of this study was to analyse the regularity of EEG time series of healthy participants undergoing a series of cognitive tasks to test the hypothesis whether the irregularity of EEG signals changes through increasing time on performing cognitive task. EEG activities were recorded from two scalp loci of the international 10-20 system (that are Fz and P­z electrodes representing the midline frontal and parietal lobes of the brain respectively) in twelve participants from which Approximate Entropy (ApEn) values were computed. ApEn is a nonlinear method which quantifies the irregularity of a time series whereby larger ApEn corresponds to more irregularity. ApEn values were found to be significantly different among the six time intervals of a series of 5-minutes cognitive tasks (p<0.01). Moreover, there was a significant positive correlation between ApEn at the Pz electrode and measured mental fatigue visual analogue scale (p<0.01). Therefore, the irregularity found in the participants’ EEG signals across the time intervals of performing cognitive task demonstrate that EEG regularity analysis with ApEn might be a useful tool in increasing our insight into the characteristics of the brain processes involved while performing fatiguing cognitive task and in quantifying cognitive fatigue.

Keywords: approximate entropy, attention, cognitive fatigue, EEG analysis, nonlinear method

##plugins.themes.academic_pro.article.details##

Author Biography

Dineshen Chuckravanen, University of Northumbria at Newcastle, Newcastle, Tyne and Wear, NE1 8ST, United Kingdom.

Researcher Investigating Neuromarkers for mental Fatigue States in Collaboration with the School of Engineering and School of Life Sciences at Northumbria University.
How to Cite
Chuckravanen, D. (2014). Approximate Entropy as a Measure of Cognitive Fatigue: An EEG Pilot Study. International Journal of Emerging Trends in Science and Technology, 1(07). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/304

References

[1] R.F. Baumeister, “Ego Depletion and Self-Control Failure: An Energy Model of the Self’s Executive Function,” Self and Identity, 1, pp. 129-136, 2002.

[2] W.O. Tatum, A.M. Husain, S.R. Benbadis, ‘Handbook of EEG Interpretation,’ Demos Medical Publishing, 2008.

[3] H. Aurlien, I.O. Gjerde, J.H. Aarseth, B. Karlsen, H. Skeidsvoll, N.E. Gilhus ‘EEG background activity described by a large computerized database’, Clinical Neurophysiology 115 (3), pp. 665–673, 2004.

[4] S.M. Slobounov, K. Fukada, R. Simon, M. Rearick, and W. Ray, ‘Neurophysiological and behavioral indices of time pressure effects on visuomotor task performance,’ Brain Res Cogn., 9, pp. 287–298, 2000.

[5] M.E. Smith, L.K. McEvoy, A. Gevins (1999), ‘Neurophysiological indices of strategy development and skill acquisition’, Cogn. Brain Res, 7, pp. 389-404, 1999.

[6] J. Jeong, “EEG dynamics in patients with Alzheimer’s disease,” Clinical Neurophysiology, vol. 115, pp. 1490–1505, 2004.

[7] M. Sabeti (2009). "Entropy and complexity measures for EEG signal classification of schizophrenic and control participants". Artificial Intelligence in Medicine 47 (3): 263–274. doi:10.1016/j.artmed.2009.03.003. PMID 19403281.

[8] J. P. Eckmann and D. Ruelle, “Fundamental limitations for estimating dimensions and Lyapunov exponents in dynamical systems,” Physica D, vol. 56, pp. 185–187, 1992.

[9] D. Abasolo, R. Hornero, and P. Espino, ‘Approximate Entropy of EEG Background Activity in Alzheimer’s disease patients,’ Intelligent Automation and Soft Computing, 15(4), pp. 591-603, 2009.

[10] S. M. Pincus and A. L. Goldberger, “Physiological time series analysis: what does regularity quantify?” American Journal of Physiology (Heart and Circulatory Physiology), vol. 266, pp. H1643–H1656, 1994.

[11] S. M. Pincus, “Approximate entropy as a measure of system complexity,” Proceedings of the National Academy of Sciences of the USA, vol. 88, pp. 2297–2301, 1991.

[12] C. J. Stam, T.C.A.M. van Woerkon, and W.S. Pritchard, ‘Use of nonlinear EEG measures to characterize EEG changes during mental activity,”Electroencephalography and Clinical Neurophysiology, vol. 76, pp. 214–224, 1996.

[13] L. Capuron, L. Welberg, C. Heim, D. Wagner, L. Solomon, D.A. Papanicolaou, R.C. Craddock, A.H. Miller, W.C. Reeves, ‘Cognitive dysfunction related to subjective report of mental fatigue in patients with chronic fatigue syndrome’, Neuropsychopharmocology, 2005.

[14] M.A.S. Boksem, T.F. Meijman, M.M. Lorist, ‘Mental fatigue, motivation and action monitoring’, J. Bio. Psycho., 72, pp. 123-132, 2006.

[15] A. Holm, K. Lukander, J. Korpela, M. Sallinen, and K.M.I. Muller KMI, ‘Estimating brain load from the EEG’, The Scientific World Journal, vol. 9, pp. 639-651, 2009.

[16] A. Erfanian, and B. Mahmoudi, ‘Real-time ocular artifact suppression using recurrent neural network for electro-encephalogram based brain-computer interface,’ Med. Biol. Eng. Comput., 43, pp. 296-305, 2005.

[17] Quintana, S. M. & Maxwell, S. E., ‘A comparison of six estimates of epsilon in repeated measures designs with small samples sizes,’ Journal of Educational Statistics, 19, 57-71, 1994.

[18] H. Viertio-Oja,V. Maja, M. Sarkela, P. Talja, N. Tenkanen, H. Tolvanen-Laakso, M. Paloheimo, A. Vakkuri, A. Yli-Hankala, P. Merilainen, 'Description of the Entropy algorithm as applied in the Datex-Ohmeda S/5(TM) Entropy Module', Acta Anaesthesiol. Scand, 48, pp. 154-161, 2004.

[19] G. Matthews, D.R. Davies, S.J. Westerman, and R.B. Stammers RB, ‘Human performance: Cognition, stress and individual differences', Philadelphia, PA: Taylor and Francis, 2000.

[20] G. Matthews, and P.A. Desmond, P.A, 'Task-induced fatigue states and simulated driving performance', The Quaterly Journal of Experimental Psychology, 55A(2), pp. 659-686, 2002.