Key result
Support Vector Machine combined with Wavelet Packet Decomposition achieved the best performance for emotion state identification, attaining 98.2% accuracy and 99.3% AUC.
Why the study?
Brain signals measured by EEG may be utilized to characterize a wide range of emotional states.
Support Vector Machine combined with Wavelet Packet Decomposition provides highly accurate emotion recognition from EEG signals.
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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Ali et al. (2023) studied Emotion recognition. Wavelet Packet Decomposition and Machine Learning algorithms vs. Comparison among Decision Tree, Support Vector Machine, and K-Nearest Neighbor was evaluated on Emotion state identification performance. Support Vector Machine combined with Wavelet Packet Decomposition achieved the best performance for emotion state identification, attaining 98.2% accuracy and 99.3% AUC.
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