ArticleExperimental brain research2025
Analysis of frequency domain features for the classification of evoked emotions using EEG signals.
Article in Experimental brain research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Deep Hybrid CNN-BiLSTM-Attention Model for EEG Classification Using Wavelet Features.Annals of neurosciences · 2026Article
- Enhancing EEG based cognitive state classification using graph Fourier transform.AIMS neuroscience · 2026Article
- Hybrid lightweight vision transformers with attention mechanism for feature extraction and classification of product designs.PloS one · 2026Article
- Deep Learning-Based Fatigue Monitoring in Natural Environments: Multi-Level Fatigue State Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Estimation of Task-Related Dynamic Brain Connectivity via Data Inflation and Classification Model Explainability.Neuroinformatics · 2025Article
- Effects of AC induced electric fields on neuronal firing sensitivity and activity patterns.Frontiers in computational neuroscience · 2025Article
- Measuring emotions triggered by fragrance through behavioral paradigms.Frontiers in psychology · 2025Article
- An innovative brain-inspired neuronal competition model for electroencephalography emotion recognition.Digital healthArticle
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8 authors.
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Abstract
Emotion is a natural instinctive state of mind that greatly influences human physiological activities and daily life decisions. Electroencephalogram (EEG) signals created from the central nervous system are very useful for emotion recognition and classification. In this study, EEG signals of individuals are analyzed by the variational mode decomposition (VMD) for frequency domain features to recognize visual stimuli-based evoked emotions (happy, sad, fear). After cleaning EEG signals from artifacts, VMD is employed to decompose the signal into its respective intrinsic mode functions (IMFs). A sliding windowing approach is adopted to calculate the power distributions in each of the predefined frequency bands. The results reveal that extracting frequency domain features using a sliding window of 3 s significantly enhances the efficiency of analyzing induced emotions in subjects. The random forest model shows promising results in classifying various emotions, achieving an accuracy of 99.57% for validation and 99.36% for testing. Moreover, it is observed that the fifth IMF has a strong relationship with emotion elicited from visual stimuli. In addition, the features of the trained model are analyzed by Shapley additive explanations.
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