Recognition of Affective States via Electroencephalogram Analysis and Classification

  • Abeer Al-NafjanEmail author
  • Manar Hosny
  • Yousef Al-Ohali
  • Areej Al-Wabil
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 722)


Understanding and reacting to the affective state of users is increasingly becoming important in the field of human–computer interaction (HCI) research and practice. Recent developments in brain–computer interface (BCI) technology has facilitated improved accuracy in human emotion detection and classification. In this paper, we investigate the possibility of using electroencephalogram (EEG) for the detection of four affective states based on a dimensional model (valence and arousal) of emotions. We conduct rigorous offline analysis for investigating the deep neural network (DNN) classification method in emotion detection. We also compare our classification performance with a random forest (RF) classifier and support vector machine (SVM). The data analysis results revealed that the proposed DNN-based classifier method outperformed the methods based on the SVM and RF classifiers.


Electroencephalogram (EEG) Brain–Computer Interface (BCI) Emotion recognition Affective state DEAP dataset 



The study was supported by the Human–Computer Interaction (HCI) Lab at King Abdulaziz City for Science and Technology in Riyadh, Saudi Arabia.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Abeer Al-Nafjan
    • 1
    • 2
    Email author
  • Manar Hosny
    • 2
  • Yousef Al-Ohali
    • 2
  • Areej Al-Wabil
    • 3
    • 4
    • 5
  1. 1.College of Computer and Information SciencesImam Muhammad bin Saud UniversityRiyadhSaudi Arabia
  2. 2.College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  3. 3.Human-Computer Interaction (HCI) LabRiyadhSaudi Arabia
  4. 4.Center for Complex Engineering SystemsKing Abdulaziz City for Science and TechnologyRiyadhSaudi Arabia
  5. 5.Center for Complex Engineering SystemsMITCambridgeUSA

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