Advertisement

InECCE2019 pp 483-493 | Cite as

EEG Pattern of Cognitive Activities for Non Dyslexia (Engineering Student) due to Different Gender

  • E. M. N. E. M. Nasir
  • N. A. Bahali
  • N. Fuad
  • M. E. Marwan
  • J. A. Bakar
  • Danial Md Nor
Conference paper
  • 14 Downloads
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)

Abstract

The purpose of this study is to identify the electroencephalogram (EEG) pattern of male and female engineering student during the cognitive activity. EEG is a method to monitoring electrical activity in the brain and has four main brainwave signal Delta Wave, Theta Wave, Alpha Wave and Beta Wave. Delta wave is a slow wave generated in deepest meditation, Theta Wave usually occurs in sleep, Alpha Wave dominant in calming, relaxing condition and Beta Wave dominant in wakeful condition. The raw data collected analysis using SPSS and Microsoft Excel to analysis the accuracy and the brainwave pattern between male and female. The average, standard derivation, correlation and Q-Q Plot are used to identify the EEG pattern between male and female during cognitive activity. Cognitive is one of the bloom taxonomy formulate for education activities. The process involves in decision making, understanding of information, attitudes and solving. Subjects are given a set of question to answer. A total of 24 students, 12 males and 12 female involve recording their EEG signal while answering the cognitive question by wearing the Emotive Insight device. All subjects are from UTHM engineering students. Data collected are focused in Alpha Wave and Beta wave which exist in when someone is in awaken condition. The difference between male and female brainwave during the cognitive activity can be observed from the analysis and discussion of the result. For future recommendation for this research is the number of subject can be increased to get more accurate data.

Keywords

Electroencephalogram (EEG) Alpha wave Beta wave Cognitive Male Female 

Notes

Acknowledgement

E. M. N. E. M. Nasir and team would like to thank the Research Management Centre (RMC), Universiti Tun Hussein Onn Malaysia (UTHM) for Tier grant code H268 and GPPS grant code H460 for this research. The gratification is also dedicated towards Faculty of Electrical and Electronic Engineering (FKEE) and members of Artificial Intelligent Laboratory, FKEE, UTHM for their cooperation and kindness. Appreciation also goes to Brainwave Research Group (BRG) for their support.

References

  1. 1.
    Kolb B, Whishaw IQ (2008) Fundamental of human neuropsychology. Worth Publishers, New York, USAGoogle Scholar
  2. 2.
    Murat ZH, Taib MN, Lias S, Abdul Kadir RSS, Sulaiman N, Mustafa M (2010) The conformity between brainwave balancing index (BBI) using EEG and psychoanalysis test. Int J Simul Syst Sci Technol 11:85–91Google Scholar
  3. 3.
    Michael C (2014) Corballis: left brain, right brain: facts and fantasies. Published: January 21 https://doi.org/10.1371/journal.pbio.1001767
  4. 4.
    Huerta-Pacheco NS, Rebolledo-Mendez G, Hernandez-Gonzalez S (2016) Cognitive-affective modelling approach in tutoring system. In: 1st international workshop on emotional awareness in software engineering, pp 1–4Google Scholar
  5. 5.
    Gj1ska B, M-Simoska S, Hinrikus H, Pop-Jordanova N, Pop-Jordanov J (2016) Brain topography of EMF-induced EEG—changes in restful wakefulness: tracing current effects, targeting, future prospects. Contributions Sec Med Sci, XXXVI 3:103–111Google Scholar
  6. 6.
    Steven RH, Galloway T, Borka C (2007) EEG related changes in cognitive workload, engagement and distraction as students acquire problem solving skills. Team Neorodynamic by Learning Chameleon Inc.Google Scholar
  7. 7.
    Teplan M (2002) Fundamentals of EEG measurement. Measur Sci Rev 2:1–11Google Scholar
  8. 8.
    Dahal N, Nandagopal N, Nafalski A, Nedic Z (2011) Modeling of cognition using EEG: a review and a new approach. In: IEEE region conference, pp 1045–1049Google Scholar
  9. 9.
    Fuad N, Jailani R, Omar WRW, Jahidin AH, Taib MN (2012) Three dimension 3D signal for electroencephalographic (EEG). In: Proceedings—2012 IEEE control and system graduate research colloquium, ICSGRC 2012, pp 262–266Google Scholar
  10. 10.
    Berka C, Levendowski DJ, Lumicao MN, Yau A, Davis G, Zivkovic VT, Olmstead RE, Tremoulet PD, Craven PL (2007) EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks. Aviat Space Environ Med 1–15Google Scholar
  11. 11.
    Anderson LW, Krathwohl DR (2002) A taxonomy for learning, teaching and assessing: a revision of Bloom’s taxonomy of educational objectives (Complete edition). Longman, New York, pp 1–35 (2001)Google Scholar
  12. 12.
    Plechawska-Wójcik M, Wawrzyk M, Wesołowska K, Kaczorowska M, Tokovarov M, Dmytruk R, Borys M (2011) EEG spectral analysis of human cognitive workload study. Studia InformaticaGoogle Scholar
  13. 13.
    Bell MA, Cuevas K (2012) Using EEG to study cognitive development: issues and practices. In: US national library of medicine national institutes of health. J Cogn Dev 13(3):281–294Google Scholar
  14. 14.
    Fundamentals of EEG Measurement (2002) M Teplan. Measur Sci Rev 2:1–11Google Scholar
  15. 15.
    Nishizawa S, Benkelfat C, Young SN, Leyton M, Mzengeza SD, Montigny C, de Blier P, Diksic M (1997) Differences between males and females in rates of serotonin synthesis in human brain. In: US national library of medicine national institutes of health 94(10):5308–5313Google Scholar
  16. 16.
    Jahidin AH, Taib MN, Megat Ali MSA, Md Tahir N, Lias S, Haron MH, R MohdIsa, Omar WRW, Fuad N (2013) Evaluation of brainwave sub-band spectral centroid in human intelligence. In: IEEE 9th international colloquium on signal processing and its applications, 8–10 March 2013, Kuala Lumpur, Malaysia, pp 295–298Google Scholar
  17. 17.
    Nielsen JA, Zielinski BA, Ferguson MA, Lainhart JE, Anderson JS (2013) An evaluation of the left-brain versus right-brain hypothesis with resting state functional connectivity magnetic resonance imaging. Published: 14 August 2013, https://doi.org/10.1371/journal.p1.0071275
  18. 18.
    Fuad N, Taib MN, Jahidin AH, Mohd Isa R, Marwan ME (2013) Brainwave sub-band power spectral density characteristics for human brain balanced via three dimensional electroencephalographic model. In: The 15th international conference on biomedical engineering (ICBME2013), pp 543–545. National University of Singapore University Town, Singapore, DecemberGoogle Scholar
  19. 19.
    Che-Him N, Nor ME, Md Kamaruddin NK, Asrah NM, Saharan S, Khalid K (2014) Engineering statistic. Universiti Tun Hussein Onn MalaysiaGoogle Scholar
  20. 20.
    Cheryl Bagley Thompson Ph.D., RN (2009) Descriptive data analysis. Air Med J 28:2 March–April 2009. MS-52Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • E. M. N. E. M. Nasir
    • 1
    • 2
  • N. A. Bahali
    • 1
    • 2
  • N. Fuad
    • 1
    • 2
  • M. E. Marwan
    • 2
    • 3
  • J. A. Bakar
    • 1
    • 2
  • Danial Md Nor
    • 1
  1. 1.Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  2. 2.Brainwave Research Group, Faculty of Electrical and Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaParit RajaMalaysia
  3. 3.Kolej Poly-Tech MARA Batu PahatBatu PahatMalaysia

Personalised recommendations