Key result
Cubic SVM achieves ~96% accuracy for subject-dependent EEG emotion classification.
Machine learning classifiers using statistical features from EEG signals can effectively recognize human emotions with high accuracy.
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High subject-dependent EEG accuracy suggests clinical monitoring potential; leaves open subject-independent validation for cardiovascular applications.
Dewangan et al. (2023) studied Emotion recognition (n=15). Support vector machine (SVM) classifiers for EEG signals was evaluated on Emotion classification accuracy. Subject-dependent emotion classification using a cubic support vector machine achieved an average accuracy of 95.73%, while subject-independent analysis achieved up to 83.7% accuracy.
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