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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)