Skip to main content
Book cover

Pacific Rim Knowledge Acquisition Workshop

PKAW 2019: Knowledge Management and Acquisition for Intelligent Systems pp 21–31Cite as

Neurofeedback and AI for Analyzing Child Temperament and Attention Levels

Part of the Lecture Notes in Computer Science book series (LNAI,volume 11669)

Abstract

One of the common problems among preschool children is attention ability development. It is important to detect and identify earlier the attention problems which may minimize the harmful impact of childhood disorders. The purpose of this research is to predict and analyze the attention levels of children aged 4–7. Using parental report or subjective report to analyze the children’s psychological dimensions of temperament is a common approach for temperament research, but it may be bias. Electroencephalography (EEG) is a method to illustrate the brain electrical activity. We proposed a Neurofeedback Technology (NFT) system to amalgamate the collection of EEG signals data and Behavior Style Questionnaire (BSQ) for child temperament data by applying k-means algorithm, an Artificial Intelligence (AI) unsupervised machine learning, clustering analysis method, to observe children’s attention levels. The experimental results not only infer that the value of temperament with EEG classification could be consistent, but also provide a valid way to classify attention levels in specific time period. The combination of the parental subjective report with EEG data demonstrates a novel and valuable approach for resolving child attention problems. The results facilitate earlier identification of attention problems and support better parent-child understanding and interactions.

Keywords

  • Neurofeedback
  • Artificial Intelligence
  • EEG
  • Big data analysis
  • Attention
  • Temperament

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-30639-7_3
  • Chapter length: 11 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   44.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-30639-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   59.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

References

  1. Carey, W.B., McDevitt, S.C.: Revision of the infant temperament questionnaire. Pediatrics 61(5), 735–739 (1978). https://doi.org/10.1037/t05932-000

    CrossRef  Google Scholar 

  2. Capsi, A., Silva, P.A.: Temperamental qualities at age three predict personality trait in young adulthood: longitudinal evidence from a birth cohort. Child Dev. 66(2), 486–498 (1995). https://doi.org/10.2307/1131592

    CrossRef  Google Scholar 

  3. Cheng, S.-C., Cheng, Y.-P., Huang, C.-H., Huang, Y.-M.: Exploring the correlation between attention and cognitive load of students when attending different classes. In: Wu, T.-T., Huang, Y.-M., Shadieva, R., Lin, L., Starčič, A.I. (eds.) ICITL 2018. LNCS, vol. 11003, pp. 205–214. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99737-7_21

    CrossRef  Google Scholar 

  4. Clarivate Analytics, Essential Science Indicators. https://clarivate.com.tw/products/essential-science-indicators/

  5. Desai, J.: Electroencephalography (EEG) Data Collection and Processing through Machine Learning, University of Arkansas, Theses and Dissertations (2014)

    Google Scholar 

  6. Felzer, T., Freisleben, B.: BRAINLINK: a software tool supporting the development of an EEG-based brain-computer interface. Proc. METMBS 2, 329–335 (2002)

    Google Scholar 

  7. Heilmeyer, F.A., Schirrmeister, R.T., Fiederer, L.D., Volker, M., Behncke, J., Ball, T.: A large-scale evaluation framework for EEG deep learning architectures. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1039–1045. IEEE (2018). https://doi.org/10.1109/smc.2018.00185

  8. Hegde, N.N., Nagananda, M.S., Harsha, M.: EEG signal classification using k-means and fuzzy c means clustering methods. Int. J. Sci. Technol. Eng. 2(1), 1–5 (2015)

    CrossRef  Google Scholar 

  9. Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 881–892 (2002). https://doi.org/10.1109/TPAMI.2002.1017616

    CrossRef  MATH  Google Scholar 

  10. Mathewson, K.J., Miskovic, V., Schmidt, L.A.: Individual differences in temperament: definition, measurement, and outcomes. In: Ramachandran, V.S. (ed.) Encyclopedia of Human Behavior (Second Edn.), pp. 418–425. Academic Press, San Diego (2012). https://doi.org/10.1016/b978-0-12-375000-6.00203-2

    CrossRef  Google Scholar 

  11. Mahone, E.M., Schneider, H.E.: Assessment of attention in preschoolers. Neuropsychol. Rev. 22(4), 361–383 (2012). https://doi.org/10.1007/s11065-012-9217-y

    CrossRef  Google Scholar 

  12. Orhan, U., Hekim, M., Ozer, M.: EEG signals classification using the k-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38(10), 13475–13481 (2011). https://doi.org/10.1016/j.eswa.2011.04.149

    CrossRef  Google Scholar 

  13. Palfrey, J.S., Levine, M.D., Walker, D.K., Sullivan, M.D.: The emergence of attention deficits in early childhood: a prospective study. J. Dev. Behav. Pediatr. 6(6), 339–348 (1985). https://doi.org/10.1097/00004703-198512000-00004

    CrossRef  Google Scholar 

  14. Reed, M.A., Pien, D.L., Rothbart, M.K.: Inhibitory self-control in preschool children. Merrill-Palmer Q. 30(2), 131–147 (1984)

    Google Scholar 

  15. Rothbart, M.K., Bates, J.E.: Temperament. In: Damon, W., Lerner, R. (Series eds.) (eds.) Handbook of Child Psychology, vol. 3, Social, Emotional, and Personality Development, 6th edn., pp. 99–166. Wiley, New York (2006)

    Google Scholar 

  16. Singh, D., Reddy, C.K.: A survey on platforms for big data analytics. J. Big Data 2(1), 8 (2015). https://doi.org/10.1186/s40537-014-0008-6

    CrossRef  Google Scholar 

  17. Sonuga-Barke, E.J., Koerting, J., Smith, E., McCann, D.C., Thompson, M.: Early detection and intervention for attention-deficit/hyperactivity disorder. Expert Rev. Neurother. 11(4), 557–563 (2011). https://doi.org/10.1586/ern.11.39

    CrossRef  Google Scholar 

  18. Thomas, A., Chess, S.: Temperament and Development. Brunner/Mazel, New York (1977)

    Google Scholar 

  19. Wang, L.: Mu-ming Poo: China brain project and the future of Chinese neuroscience. Nat. Sci. Rev. 4(2), 258–263 (2017). https://doi.org/10.1093/nsr/nwx014

    CrossRef  Google Scholar 

  20. Wang, P.L. (ed.): Children’s Temperament: Basic Characteristics and Social Composition. Psychological Publishing, Taiwan (2003)

    Google Scholar 

  21. Wang, P.L.: A literature review of child temperament research from 1980 to 2011. Res. Appl. Psychol. 61, 52–112 (2014)

    Google Scholar 

  22. Wilens, T.E., et al.: Psychiatric comorbidity and functioning in clinically referred preschool children and school-age youths with ADHD. J. Am. Acad. Child Adolesc. Psychiatry 41(3), 262–268 (2002). https://doi.org/10.1097/00004583-200203000-00005

    CrossRef  Google Scholar 

  23. Zentner, M., Bates, J.E.: Child temperament: an integrative review of concepts, research programs, and measures. Int. J. Dev. Sci. 2(1–2), 7–37 (2008). https://doi.org/10.3233/dev-2008-21203

    CrossRef  Google Scholar 

  24. Zhu, G., Li, Y., (Paul) Wen, P., Wang, S., Zhong, N.: Unsupervised classification of epileptic EEG signals with multi scale k-means algorithm. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds.) BHI 2013. LNCS (LNAI), vol. 8211, pp. 158–167. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02753-1_16

    CrossRef  Google Scholar 

Download references

Acknowledgement

This research is supported by the Ministry of Education, Taiwan and Shih Chien University under grant USC-107-03-04010, USC-107-05-04006 and USC-108-08-04005.

Author information

Authors and Affiliations

  1. Shih Chien University, Taipei, 104, Taiwan

    Maria R. Lee, Anna Yu-Ju Yen & Lun Chang

Authors
  1. Maria R. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anna Yu-Ju Yen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lun Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anna Yu-Ju Yen .

Editor information

Editors and Affiliations

  1. Aoyama Gakuin University, Tokyo, Japan

    Kouzou Ohara

  2. University of Tasmania, Tasmania, Australia

    Quan Bai

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Lee, M.R., Yen, A.YJ., Chang, L. (2019). Neurofeedback and AI for Analyzing Child Temperament and Attention Levels. In: Ohara, K., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2019. Lecture Notes in Computer Science(), vol 11669. Springer, Cham. https://doi.org/10.1007/978-3-030-30639-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30639-7_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30638-0

  • Online ISBN: 978-3-030-30639-7

  • eBook Packages: Computer ScienceComputer Science (R0)