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Mental Stress Level Classification: A Review

IJCA Proceedings on International Conference on Advances in Science and Technology
© 2015 by IJCA Journal
ICAST 2014 - Number 1
Year of Publication: 2015
Radhika Deshmukh

Radhika Deshmukh and Manjusha. Article: Mental Stress Level Classification: A Review. IJCA Proceedings on International Conference on Advances in Science and Technology ICAST 2014(1):15-18, February 2015. Full text available. BibTeX

	author = {Radhika Deshmukh and Manjusha},
	title = {Article: Mental Stress Level Classification: A Review},
	journal = {IJCA Proceedings on International Conference on Advances in Science and Technology},
	year = {2015},
	volume = {ICAST 2014},
	number = {1},
	pages = {15-18},
	month = {February},
	note = {Full text available}


Electroencephalography (EEG) is the tool to record electrical activity over the scalp. This technique is widely used in clinical or research setting, since it is user friendly and non – invasive. In clinical setting, the EEG signal is used to diagnose the disease related to brain. In research setting, the usage of EEG signal is focused on rehabilitation; mental stress study . This paper presented the review on different methods for mental stress level classification. There are four methods for investigation such as principal component analysis, artificial neural network, discrete wavelet transform and spectral centroid technique. The features obtained from methods were extracted from recorded EEG signals and modeled using various classifiers like k-NN and ANN classifier. Based on this four method, we concluded that principal component analysis is better method and it has high accuracy. (98%).


  • N. Sulaiman, M. N. Taib, S. Lias, Z. H. Murat, S. A. M. Aris, and N. H. A. Hamid, "EEG-based Stress Features Using Spectral Centroids Technique and k-Nearest Neighbor Classifier," 2011 UkSim 13th International Conference on Computer Modelling and Simulation, pp. 69–74, Mar. 2011.
  • P. Karthikeyan, M. Murugappan, and S. Yaacob, "A Study on Mental Arithmetic Task based Human Stress Level Classification Using Discrete Wavelet Transform," no. October, pp. 77–81, 2012.
  • S. A. Awang, P. Mp, and S. Yaacob, "Implementing Eigen Features methods / neural network for EEG signal analysis," in 'International Conference on Intelligent Systems and Control, 2013.
  • H. Yang, Y. Wang, C. -J. Wang, and H. -M. Tai, "Correlation dimensions of EEG changes during mental tasks," in Conference proceedings_: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2004, vol. 1, no. 1, pp. 616–619.
  • X. Liu, K. Iwanaga, and S. Koda, "Circulatory and central nervous system responses to different types of mental stress. ," Industrial health, vol. 49, no. 3, pp. 265–73, Jan. 2011.
  • Saidatul Ardeenawatie, Paul Murugesa Pandiyan, Sazali Yaacob, "Mental Stress Level Classification Using Eigenvector Features And Principle Component Analysis" . Vol. 3 Iss. 5,PP. 254-261, May 2013
  • M. R. Y. Zoshk and M. Azarnoosh, "The Measurement and Processing of EEG Signals to Evaluate Fatigue," pp. 258– 261, 2010.
  • M. Murugappan, "Inferring of Human Emotional States using Multichannel EEG," European Journal of Scientific Research, vol. 48, no. 2, pp. 281–299, 2010.
  • S. A. Hosseini, M. A. Khalilzadeh, M. B. Naghibi-Sistani, and V. Niazmand, "Higher Order Spectra Analysis of EEG Signals in Emotional Stress States," 2010 Second International Conference on Information Technology and Computer Science, pp. 60–63, Jul. 2010.
  • C. J. Stam, T. C. van Woerkom, and W. S. Pritchard, "Use of non-linear EEG measures to characterize EEG changes during mental activity. ," Electroencephalography and clinical neurophysiology, vol. 99, no. 3, pp. 214–24, Sep. 1996.
  • E. Verona, N. Sadeh, and J. J. Curtin, "Stress-Induced Asymmetric Frontal Brain Activity and Aggression Risk," Journal of Abnormal Psychology, vol. 118, no. 1, pp. 131–145, 2009.
  • T. Hayashi, "Anterior brain activities related to emotional stress," Stress: The International Journal on the Biology of Stress, vol. 80, pp. 8–13, 2006.
  • R. S. Lewis, N. Y. Weekes, and T. H. Wang, "The effect of a naturalistic stressor on frontal EEG asymmetry, stress, and health. ,"Biological psychology, vol. 75, no. 3, pp. 239–47, Jul. 2007.
  • P. Karthikeyan, M. Murugappan, and S. Yaacob, "A Review on Stress Inducement Stimuli for Assessing Human Stress Using Physiological Signals," Blood Pressure, pp. 446–451, 2011.
  • Prof. Shamla Mantri, Vipul Patil, Rachana Mitkar" EEG Based Emotional Distress Analysis – A Survey". International Journal of Engineering Research and Development e-ISSN: 2278-067X, p-ISSN: 2278-800X, www. ijerd. com Volume 4, Issue 6 (October 2012), PP. 24-28.
  • R. Khosrowabadi, C. Q. Hiok, Abdul Wahab and K. A. Kai, "EEGbased emotion recognition using self-organizing map for boundary detection", International Conference on Pattern Recognition, pp. 4242-4245, 2010.
  • J. Zhai and A. Barreto, "Stress Detection in Computer Users Based on Digital Signal Processing of Noninvasive Physiological Variables," in Engineering in Medicine andBiology Society, 2006. EMBS /06. 28th Annual International Conference of the iEEE, 2006, pp. 1355-1358.