Why the study?
Extracting hidden patterns in EEG signals is challenging due to their nonstationary nature, making it difficult to identify emotions using EEG.
Population
DEAP EEG emotion dataset
Comparison
MEEMD vs EEMD
Key result
A feature extraction method based on ensemble median empirical mode decomposition achieved accuracy rates of 74.3% for valence and 78% for arousal classes in decoding EEG signals.
Authors
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May aid EEG emotion decoding; leaves open clinical validation before any practice adoption.
A feature extraction method based on MEEMD achieved accuracy rates of 74.3% for valence and 78% for arousal in emotion recognition using EEG signals.
Samal et al. (2023) studied Emotion recognition. Ensemble median empirical mode decomposition (MEEMD) vs. Ensemble empirical mode decomposition (EEMD) was evaluated on Accuracy rates for valence and arousal classes. A feature extraction method based on ensemble median empirical mode decomposition achieved accuracy rates of 74.3% for valence and 78% for arousal classes in decoding EEG signals.