Adaptive Emotion Detection Using the Valence-Arousal-Dominance Model and EEG Brain Rhythmic Activity Changes in Relevant Brain Lobes

Sofien Gannouni, Arwa Aledaily, Kais Belwafi, Hatim Aboalsamh

    Research output: Contribution to journalArticlepeer-review

    16 Scopus citations

    Abstract

    Contrary to popular belief that only the frontal lobe is concerned with emotions, recent neuroscience experiments show that in each emotional state, some of the brain lobes fired clearly whereas some did not. Unlike most of the previous works, which focused on choosing a fixed set of electrodes to detect emotions, this work presents a novel approach that consists of investigating the cerebral activity while experiencing emotions to identify the brain lobes that are showing significant and relevant changes. The identification process of relevant brain lobes is adaptive as the brain activity varies from one person to another and differs from one lobe to another during the same emotional state. The electrodes of the selected brain lobes will be tuned before being used as resources to extract the features that are required for the proposed three phases classification process. Using a clustering technique, the tuning process excludes every electrode that hardly separates between relevant and irrelevant rhythmic brain changes. The results show that the proposed method outperforms all previous approaches. Compared to the best performance obtained by previous studies, the proposed method enhanced the accuracy of both valence and arousal dimensions by 5%. Besides, the accuracy of the dominance dimension was improved by 2%.

    Original languageBritish English
    Article number9060904
    Pages (from-to)67444-67455
    Number of pages12
    JournalIEEE Access
    Volume8
    DOIs
    StatePublished - 2020

    Keywords

    • Brain-computer interfaces
    • EEG
    • machine learning
    • multi-class classification
    • one-class classifier
    • VAD model

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