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Table 1 Summary of studies conducted on EEG-based emotion recognition using entropy as a feature

From: EEG-based human emotion recognition using entropy as a feature extraction measure

Reference No. of subjects Emotions Features Database Classifier Accuracy
[30] 3 men, 3 women Positive, negative ES, DE, DASM, RASM Private SVM
KNN
76.56%
84.22%
[77] Positive, negative Sample entropy SVM weight classifier 85.11%
[51] 16 men,          16 women Arousal, valence Wavelet entropy DEAP SVM 65%
[29] 7 men, 8 women Positive, neutral, negative Dynamic sample entropy SEED SVM 64.15%
[50] 6 men, 7 women Positive, neutral, negative Power spectral entropy, correlation dimension Private SVM 79.58%
82.58%
[76] 5 men Happy, neutral, disgust RAQA; Shannon’s entropy and 5 others eNTERFACE06_EMOBRAIN Multilayer perception           36%
Time-delay neural network 36%
Probabilistic neural network 99.96%
[75] 5 Happy, sadness, fear RAQA; entropy and 5 others Private SVM 92.24%
[3] 16 men,          16 women Excitement, happiness, sadness, hatred Shannon’s entropy and 3 others DEAP Multiclass SVM 94.097%
[33] 5 men, 5 women Happy, calm, sad, fear EMD approximate entropy Private Integration of deep belief network and SVM (DBN-SVM) 87.32%
[4] 16 men,          16 women Excitement, happy, sadness, hatred Approximate entropy, K-S entropy, permutation entropy, singular entropy, Shannon’s entropy DEAP                                     SVM 59.8%                
7 men, 8 women Positive, neutral, negative Spectral entropy and 12 other nonlinear entropy methods SEED 83.33%
[71] 16 men,          16 women 2 and 3 level of labeling in arousal and valence space Multiscale fuzzy entropy DEAP SVM 2-class
90.81% (A)
90.53% (V)
3-class
79.83% (A)
77.80% (V)
[74] 16 men,          16 women HAHV, HALV, LAHV, LALV EMD
Sample entropy
DEAP SVM 94.98% (binary class)
93.20% (multiclass)