September 10, 2025Scientific ReportsOpen Access

Segmentation-enhanced approach for emotion detection from EEG signals using the fuzzy C-mean and SVM

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This analysis demonstrates emotion detection using fuzzy C-means and SVM models, highlighting kernel function importance in EEG signals.

The linear kernel achieved the highest accuracy of 97.66% in emotion classification from EEG signals.
Fuzzy C-means and SVM methods were successfully combined, utilizing precision, recall, and F1-scores for performance evaluation.
One-way ANOVA analysis confirmed the linear kernel's superior performance compared to other kernel types (p < 0.05).
Deep learning models performed similarly but showed less accuracy than the optimized SVM approach for emotion recognition.

Mahmood et al. (2025) studied this question.

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September 10, 2025Scientific ReportsOpen Access

View Full Paper

Ask AI

Bookmark

Authors

This analysis demonstrates emotion detection using fuzzy C-means and SVM models, highlighting kernel function importance in EEG signals.

The linear kernel achieved the highest accuracy of 97.66% in emotion classification from EEG signals.
Fuzzy C-means and SVM methods were successfully combined, utilizing precision, recall, and F1-scores for performance evaluation.
One-way ANOVA analysis confirmed the linear kernel's superior performance compared to other kernel types (p < 0.05).
Deep learning models performed similarly but showed less accuracy than the optimized SVM approach for emotion recognition.

Mahmood et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark