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    Hybridization of Wavelet Decomposition and Machine Learning for Brain Waves based Emotion Recognition

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    Author
    Ali, Mirna
    Mian Qaisar, Saeed
    Anurulafchar, Tamanna
    Subject
    Emotion recognition
    Feature extraction
    Wavelet Decomposition
    Emotion categorization
    Machine learning
    Electroencephalography (EEG)
    Date
    2023-04-01
    
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    Abstract
    Emotion recognition has sparked the interest of researchers from a variety of disciplines. Studies have demonstrated that brain signals may be utilized to characterize a wide range of emotional states. Electroencephalogram (EEG) measures the cerebral activity. Therefore, by exploiting the EEG signals the emotion states can be determined. In this study the EEG signals undergoes through filtering, segmentation, Wavelet Packet Decomposition (WPD), feature mining, and classification. The machine learning algorithms used for classifications are “Decision Tree” (DT), “Support Vector Machine” (SVM), and K-Nearest Neighbor” (K-NN) algorithms are used for categorization. Their performance is compared for automatically identifying the emotion state. It is determined that the best performer is SVM. It has attained 98.2% accuracy, 97.3% precision, 97.3% recall, 98.7% specificity, 97.3% F1, 97.3% kappa, and 99.3% AUC.
    Department
    Electrical and Computer Engineering
    Sponsor
    Effat University
    DOI
    https://doi.org/10.1109/ICAISC56366.2023.10085288
    ae974a485f413a2113503eed53cd6c53
    https://doi.org/10.1109/ICAISC56366.2023.10085288
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