Cognitive Processing

, Volume 20, Issue 4, pp 405–417 | Cite as

Improving the accuracy of EEG emotion recognition by combining valence lateralization and ensemble learning with tuning parameters

  • Evi Septiana PaneEmail author
  • Adhi Dharma Wibawa
  • Mauridhi Hery Purnomo
Research Article


For emotion recognition using EEG signals, the challenge is improving accuracy. This study proposes strategies that concentrate on incorporating emotion lateralization and ensemble learning approach to enhance the accuracy of EEG-based emotion recognition. In this paper, we obtained EEG signals from an EEG-based public emotion dataset with four classes (i.e. happy, sad, angry and relaxed). The EEG signal is acquired from pair asymmetry channels from left and right hemispheres. EEG features were extracted using a hybrid features extraction from three domains, namely time, frequency and wavelet. To demonstrate the lateralization, we performed a set of four experimental scenarios, i.e. without lateralization, right-/left-dominance lateralization, valence lateralization and others lateralization. For emotion classification, we use random forest (RF), which is known as the best classifier in ensemble learning. Tuning parameters in the RF model were done by grid search optimization. As a comparison of RF, we employed two prevalent algorithms in EEG, namely SVM and LDA. Emotion classification accuracy increased significantly from without lateralization to the valence lateralization using three pairs of asymmetry channel, i.e. T7–T8, C3–C4 and O1–O2. For the classification, the RF method provides the highest accuracy of 75.6% compared to SVM of 69.8% and LDA of 60.4%. In addition, the features of energy–entropy from wavelet are important for EEG emotion recognition. This study yields a significant performance improvement of EEG-based emotion recognition by the valence emotion lateralization. It indicates that happy and relaxed emotions are dominant in the left hemisphere, while angry and sad emotions are better recognized from the right hemisphere.


Emotional recognition Brain signals Lateralization Brain asymmetry Random forest 



We would like to thank the Indonesia Endowment Fund for Education (LPDP), the Republic of Indonesia, which has provided funding through the Indonesia Education Scholarship granted to the first author.


This study was funded by Indonesia Endowment Fund for Education, contract number PRJ-2173/LPDP/2015.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors. The data being used in this article is the public dataset which can be accessed online and also cited as one of the references.

Informed consent

Informed consent was obtained from all individual participants included in the study performed by the dataset creator, not the author of these articles.


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Copyright information

© Marta Olivetti Belardinelli and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Evi Septiana Pane
    • 1
    • 3
    Email author
  • Adhi Dharma Wibawa
    • 1
    • 2
  • Mauridhi Hery Purnomo
    • 1
    • 2
  1. 1.Department of Electrical EngineeringInstitut Teknologi Sepuluh NopemberSurabayaIndonesia
  2. 2.Department of Computer EngineeringInstitut Teknologi Sepuluh NopemberSurabayaIndonesia
  3. 3.Industrial Training and Education of SurabayaMinistry of IndustrySurabayaIndonesia

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