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Communication Dans Un Congrès Année : 2023

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

Résumé

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.
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Dates et versions

hal-04093070 , version 1 (09-05-2023)

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Citer

Mirna Ali, Saeed Mian Qaisar, Tamanna Anurulafchar. Hybridization of Wavelet Decomposition and Machine Learning for Brain Waves based Emotion Recognition. 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC), Jan 2023, Jeddah, Saudi Arabia. pp.1-5, ⟨10.1109/ICAISC56366.2023.10085288⟩. ⟨hal-04093070⟩
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