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Table 2 Comparison of different schemes of linear classifier

From: Emotion classification based on brain wave: a survey

Schemes

Preprocessing

Feature extraction

Feature smoothing

Classification

Emotion states

Accuracy

Method by Li et al.

FT

CSP

 

liner-SVM

Happiness and sadness

93.5%

Method by Murugappan et al.

Surface Laplacian filtering

Zero mean unit variance

Wavelet transform

 

KNN and LDA

Disgust, happy, surprise, fear and neutral

KNN: 77.68%

LDA: 73.5%

Method by Wang et al.

 

Wavelet transform, PCA, LDA, CFS

LDS

liner-SVM

Negative and positive

87.53%

Method by Petrantonakis et al.

 

Statistical values, wavelet transform and HOC

 

QDA, KNN, MD and SVMs

Happiness, surprise, anger, fear, disgust and sadness

QDA: 62.3%

SVMs: 83.33%

MD: 44.90%

KNN: 34.60%

Method by Duan et al.

 

DE, DASM, RASM and ES

LDS

PCA and MRMR

liner-SVM and kNN

Negative and positive

liner-SVM: 74.10%

kNN: 69.24%