Identification of Appropriate Channels and Feature Types That Differentiate the Normal and Stress Data of EEG Signals


  • Nikita R. Hatwar Research Scholar, Information Technology, Yeshwantrao Chavan College of Engineering, Hingna Road Wanadongri, Nagpur, Maharashtra 441110, India
  • Ujwalla G. Gawande Associate Professor and Dean R & D, Information Technology, Yeshwantrao Chavan College of Engineering, Hingna Road Wanadongri, Nagpur, Maharashtra 441110, India
  • Chetana B. Thaokar Assistant Professor, Information Technology, Ramdeobaba College of Engineering, Nagpur, Maharashtra 440013, India.
  • Rajendra F. Hatwar Joint Director (IT) / Scientist-D, NIC- IVFRT, Collector Office, National Informatics Centre, Civil Lines, Nagpur - 440001, Maharashtra, India.


EEG, paired t-test, FFT, PSD, MIST, MATT, Frequency Band, Stress


Mental stress is proving to be a cause for functional impairment of daily activities and it is on increase. Further, continual stress should implicate numerous disorders of mind and body. Stress increases the chances of despair, stroke, coronary failure, and cardiopulmonary arrest. Human brain is a major target of psychological pressure because it determines the context of the human mind in a threatening and demanding circumstances as shown by latest neuroscience. The objective method of determining the level of stress, taking into account the human brain, greatly increases the associated dangerous effects. Therefore, the system proposed in this paper performs electroencephalography (EEG) signal analysis. Data for stressed individuals is recorded and the signal is filtered with time domain and frequency domain-based filters. Fast Fourier Transform (FFT) algorithm is used to transform the data from time domain to frequency domain. Features namely Normalized Absolute Power, Relative Power, Normalized Peak Power and Change in Power are extracted and paired t-test is used for feature selection. Features having confidence value above 95% are chosen. Within the experimental setting, stress is induced through Mental Arithmetic Task Tool (MATT) which is popular experimental pattern found on the concept of Montreal Imaging Stress Test (MIST). When performance was evaluated of all the subjects it was observed that in normal condition the average performance is 73.71% and in stress condition it is 60.18%. So, it is evident that MATT is inducing stress as performance is reduced by average 13.53% from normal to stress. The proposed system involves EEG feature extraction and feature selection using paired t-test to various brain locations across six frequency bands for stress detection. In this paper, our aim is to compare the different types of feature values of appropriate channels and frequency band to find the confidence percentage (above threshold percentage) of feature values that helps to differentiate the normal and stress classes. The results of proposed system find the correct frequency band of appropriate channel of feature types that differentiate the normal and stress data.


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A. Abbott, ‘‘Stress and the city: Urban decay,’’ Nature vol. 490, no. 7419, pp. 162–164, 2012."

F. Lederbogen et al., ‘‘City living and urban upbringing affect neural social stress processing in humans,’’ Nature, vol. 474, pp. 498–501, Jun. 2011.

A. P. Allen, P. J. Kennedy, J. F. Cryan, T. G. Dinan, and G. Clarke, ‘‘Biological and psychological markers of stress in humans: Focus on the trier social stress test,’’ Neurosci. Biobehavioral Rev., vol. 38, pp. 94–124, Jan. 2014.

H. Ursin and H. Eriksen, ‘‘The cognitive activation theory of stress,’’ Psychoneuroendocrinology, vol. 29, no. 5, pp. 567–592, Jun. 2004.

C. Espinosa-Garcia et al., ‘‘Stress exacerbates global ischemia-induced inflammatory response: Intervention by progesterone,’’ Stroke, vol. 48, no. 1, p. ATP83, 2017.

R. S. Duman, ‘‘Neurobiology of stress, depression, and rapid acting antidepressants: Remodeling synaptic connections,’’ Depression Anxiety, vol. 31, no. 4, pp. 291–296, Apr. 2014.

S. Cohen, D.A.J. Tyrrell, A.P. Smith, negative life events, perceived stress, negative affect, and susceptibility to the common cold, J. Pers. Soc. Psychol. 64 (1993) 131–140.

J.E. Dise-Lewis, The life events and coping inventory: an assessment of stress in children, Psychosom. Med. 50 (1988) 484–499.

K.B. Koh, J.K. Park, C.H. Kim, S. Cho, Development of the stress response inventory and its application in clinical practice, Psychosom. Med. 63 (2001) 668–678.

A.F.T. Arnsten, Stress weakens prefrontal networks: molecular insults to higher cognition, Nat. Neurosci. 18 (2015) 1376–1385.

Al-shargie, F., Tang, T. B., Badruddin, N., Kiguchi, M.: Mental Stress Quantification Using EEG Signals. In: Editor, Ibrahim, F., Usman, J., Mohktar, M. S., Ahmad M., Y. International Conference for Innovation in Biomedical Engineering and Life Sciences , LNCS, vol. 56, pp. 1-5. Springer, Putrjaya, Malaysia (2015).

Quesada-Tabares, R., Molina-Cantero, A. J., G´omez-Gonz´alez, I. M., Merino-Monge, M., Castro. J. A., Cabrera-Cabrera, R.: Emotions Detection based on a Single-electrode EEG Device. In: Proceedings of the 4th International Conference on Physiological Computing Systems, pp. 89-95. Madrid (2017).

Pinegger, A., Hiebel, H., Wriessnegger, S. C., Mu ̈ller-Putz, G. R.: Composing only by thought: Novel application of the P300 brain-computer interface”, PLOS ONE Journal, 1-19 (2017).

Jebelli, H., Hwang, S., SangHyun Lee, M. : EEG Signal-Processing Frame work to Obtain High-Quality Brain Waves from an Off-the-Shelf Wearable EEG Device. ASCE Journal of Computing in Civil Engineering, 1-12 (2017).

Subhani, A. R., Mumtaz, W., Mohamed Saad, M. N. B., Kamel, N., Saeed Malik, A.: Machine Learning Framework for the Detection of Mental Stress at Multiple Levels. IEEE Access. 5, 13545-13556 (2017). doi : 10.1109/ACCESS.2017.2723622.

Jebelli, H., Khalili, M. M., Lee, S.: A Continuously Updated, Computationally Efficient Stress Recognition Framework Using Electroencephalogram (EEG) by Applying Online Multi-Task Learning Algorithms (OMTL). IEEE Journal of Biomedical and Health Informatics, 1 – 12 (2018).

C. Vidaurre, N. Krämer, B. Blankertz, A. Schlögl, Time domain parameters as a feature for EEG-based brain-computer interfaces, Neural Netw. 22 (2009) 1313–1319.

K. Kalimeri, C. Saitis, Exploring multimodal biosignal features for stress detection during indoor mobility, in: Proceedings of the 18th ACM International Conference on Multimodal Interaction, 2016, pp. 53–60.

J.F. Alonso, S. Romero, M.R. Ballester, R.M. Antonijoan, M.A. Mananas, ˜ Stress assessment based on EEG univariate features and functional connectivity measures, Physiol. Meas. 36 (2015).

H.U. Amin, W. Mumtaz, A.R. Subhani, M.N.M. Saad, A.S. Malik, Classification of EEG signals based on pattern recognition approach, Front. Comput. Neurosci. 11 (2017), 2017-November-21. [32] S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, 3 ed., Prentice Hall, 2010.

21. K. Dedovic, R. Renwick, N.K. Mahani, V. Engert, S.J. Lupien, J.C. Pruessner, The montreal imaging stress task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain, J. Psychiatry Neurosci. 30 (2005) 319–325.

K. Dedovic, A. Duchesne, J. Andrews, V. Engert, J.C. Pruessner, the brain and the stress axis: the neural correlates of cortisol regulation in response to stress, NeuroImage 47 (2009) 864–871.

J.C. Pruessner, K. Dedovic, M. Pruessner, C. Lord, C. Buss, L. Collins, et al., Stress regulation in the central nervous system: evidence from structural and functional neuroimaging studies in human populations - 2008 curt richter award winner, Psychoneuroendocrinology 35 (2010) 179–191.

C. Setz, B. Arnrich, J. Schumm, R. La Marca, G. Tröster, U. Ehlert, Discriminating stress from cognitive load using a wearable eda device, IEEE Trans. Inf. Technol. Biomed. 14 (2010) 410–417.

Girotti M, Adler SM, Bulin SE, Fucich EA, Paredes D, Morilak DA. Prefrontal cortex executive processes affected by stress in health and disease. Prog Neuropsychopharmacol Biol Psychiatry. 2018 Jul 13; 85:161-179. doi: 10.1016/j.pnpbp.2017.07.004. Epub 2017 Jul 6. PMID: 28690203; PMCID: PMC5756532.

T. Chandola, A. Heraclides, and M. Kumari, ‘‘Psychophysiological biomarkers of workplace stressors,’’ Neurosci. Biobehavioral Rev., vol. 35, no. 1, pp. 51–57, Sep. 2010.

B. L. Seaward, Managing Stress: Principles and Strategies for Health and Wellbeing, 7th ed. Boston, MA, USA: Jones & Bartlett, 2011.

G. Jun and K. G. Smitha, ‘‘EEG based stress level identification,’’ in Proc. IEEE Int. Conf. Syst., Man, Cybern. (SMC), Oct. 2016, pp. 3270–3274.

[48] C. Kirschbaum, K. M. Pirke, and D. H. Hellhammer, ‘‘The ‘Trier social stress testâĂŹ—A tool for investigating psychobiological stress responses in a laboratory setting,’’ Neuropsychobiology, vol. 28, nos. 1–2, pp. 76–81, 1993.

ARECHE, F. O. ., & Palsetty , K. . (2021). Ubiquitous Counter Propagation Network in Analysis of Diabetic Data Using Extended Self-Organizing Map. Research Journal of Computer Systems and Engineering, 2(2), 51:57. Retrieved from

Sai Pandraju, T. K., Samal, S., Saravanakumar, R., Yaseen, S. M., Nandal, R., & Dhabliya, D.(2022). Advanced metering infrastructure for low voltage distribution system in smart grid based monitoring applications. Sustainable Computing: Informatics and Systems, 35 doi:10.1016/j.suscom.2022.100691

Begum, S. . S. ., Prasanth, K. D. ., Reddy, K. L. ., Kumar, K. S. ., & Nagasree, K. J. . (2023). RDNN for Classification and Prediction of Rock or Mine in Underwater Acoustics. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 98–104.

Samad, A. . (2022). Internet of Things Integrated with Blockchain and Artificial Intelligence in Healthcare System. Research Journal of Computer Systems and Engineering, 3(1), 01–06. Retrieved from




How to Cite

Hatwar, N. R. ., Gawande, U. G. ., Thaokar, C. B. ., & Hatwar, R. F. . (2023). Identification of Appropriate Channels and Feature Types That Differentiate the Normal and Stress Data of EEG Signals. International Journal of Intelligent Systems and Applications in Engineering, 11(11s), 102–120. Retrieved from



Research Article