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
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 632)


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.


Electroencephalogram (EEG) Alpha wave Beta wave Cognitive Male Female 



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.


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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

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