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Evolution in Computational Intelligence pp 201–209Cite as

Analysis and Identification of EEG Features for Mental Stress

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1176))

Abstract

In this paper, an attempt for identification of electroencephalogram (EEG) signal features for detection of stressed mental state has been made. In present lifestyle, almost all peoples suffer from stress. Assessment and management of stress are necessary to avoid serious mental illness. By the mean of EEG signal analysis, stressed mental state is easily detected. Here, 24 EEG features including spectral, amplitude, connectivity, and range properties are extracted in different frequency bands. A well-known feature selection algorithm sequential floating backward selection (SFBS) is used to select subset of best features. Features of theta frequency band EEG signal are identified as best features for mental stress identification.

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References

  1. Seo, S.-H., Lee, J.-T.: Stress and EEG. In: Convergence and Hybrid Information Technologies. IntechOpen (2010)

    Google Scholar 

  2. Ryali, V.S.S.R., Bhat, P.S., Srivastava, K.: Stress in the Indian Armed Forces: how true and what to do? Med. J. Armed Forces India 67(3), 209 (2011)

    Google Scholar 

  3. Kadambi, P., Lovelace, J.A., Beyette, F.R.: Changes in behavior of evoked potentials in the brain as a possible indicator of fatigue in people. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2013)

    Google Scholar 

  4. Ko, L.-W., et al.: Neural oscillations in temporoparietal lobes under inhibitory control in a naturalistic situation. In: 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE (2016)

    Google Scholar 

  5. Ko, L.-W., et al.: Mobile EEG & ECG integration system for monitoring physiological states in performing simulated war game training. In: 2015 IEEE Conference on Computational Intelligence and Games (CIG). IEEE (2015)

    Google Scholar 

  6. Rezeika, A., et al.: Brain–computer interface spellers: a review. Brain Sci. 8(4), 57 (2018)

    Google Scholar 

  7. Panicker, S.S., Gayathri, P.: A survey of machine learning techniques in physiology based mental stress detection systems. Biocybern. Biomed. Eng. (2019)

    Google Scholar 

  8. Charles, R.L., Nixon, J.: Measuring mental workload using physiological measures: a systematic review. Appl. Ergon. 74, 221–232 (2019)

    Article  Google Scholar 

  9. Koelstra, S., Muhl, C., Soleymani, M., Lee, J.S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I.: DEAP: a database for emotion analysis; using physiological signals. IEEE Trans. Affect. Comput. 3(1), 18–31 (2012)

    Google Scholar 

  10. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)

    Article  Google Scholar 

  11. Toole, J.M., Boylan, G.B.: NEURAL: quantitative features for newborn EEG using Matlab. arXiv preprint arXiv:1704.05694 (2017)

  12. Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature selection. Pattern Recogn. Lett. 15(11), 1119–1125 (1994)

    Article  Google Scholar 

  13. Ververidis, D., Kotropoulos, C.: Fast and accurate feature subset selection applied into speech emotion recognition. Signal Process. 88(12), 2956–2970 (2008)

    Article  Google Scholar 

  14. Ververidis, D., Kotropoulos, C.: Feature Selection using Matlab. https://in.mathworks.com/matlabcentral/fileexchange/22970-feature-selection-using-matlab?focused=5164696&tab=function (2009)

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Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Maulana Azad National Institute of Technology, Bhopal, 462003, India

    Mitul Kumar Ahirwal

Authors
  1. Mitul Kumar Ahirwal
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Correspondence to Mitul Kumar Ahirwal .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  2. Department of Computer Science and Information Engineering, National Dong Hwa University, Hualien, Taiwan

    Sheng-Lung Peng

  3. School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  4. Department of Informatics, University of Leicester, Leicester, UK

    Yu-Dong Zhang

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Ahirwal, M.K. (2021). Analysis and Identification of EEG Features for Mental Stress. In: Bhateja, V., Peng, SL., Satapathy, S.C., Zhang, YD. (eds) Evolution in Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-15-5788-0_19

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