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