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Table 3 Reviewed papers that used GANs in emotion recognition tasks

From: Generative adversarial networks in EEG analysis: an overview

Study

Purpose

Dataset

GAN type

Evaluation metrics

Results

(with GAN)

Y. Luo et al., 2018 [56]

Enhance EEG-based emotion recognition

SEED + DEAP

cWGAN

Classification accuracy

SEED: + 2.97%

DEAP-Arousal: + 9.15%

DEAP-Valence: + 20.13%

Y. Luo et al., 2018 [57]

Enhance EEG-based emotion recognition for semi-supervised models

SEED + DEAP

WGANDA

Classification accuracy

(W.R.T: SVM)

SEED: + 30.43%

DEAP-Arousal: + 17.63%

DEAP-Valence: + 17.63%

Y. Luo et al., 2020 [59]

Enhance EEG-based emotion recognition

SEED + DEAP

cWGAN

 + 

sWGAN

Classification accuracy

SEED

cWGAN + DNN: + 8.3%

sWGAN + DNN: + 10.2%

DEAP

cWGAN + SVM: + 3.5%

sWGAN + SVM: + 5.4%

Dong and Ren, 2020 [60]

Enhance EEG-based emotion recognition

DEAP

MCLFS-GAN

Classification accuracy

(w.r.t CNN + LSTM)

SAP

MCLFS-GAN: + 14.95%

LOSO

MCLFS-GAN: + 19.52%

Fu et al., 2021 [62]

Achieve a fine mapping of EEG data directly to facial images

SEED

Ac-GAN

• Classification accuracy

• Reliability

• Entropy

• Generated images from EEG 82.14%

• 92.02%

• 7.41

Liang et al. 2021 [63]

Fuse the spatial and temporal dynamic

brain information into a better feature representation

SEED + DEAP + MAHNOB-HCI

CNN + RNN + GAN

• Classification accuracy

• F1 score

• Up to + 7.69%

• Up to + 5.07

Pan and Zheng 2021 [64]

Enhance EEG-based emotion recognition with sample scarcity and category imbalance issues

DEAP + MAHNOB-HCI

PSD-GAN

Recognition accuracy

• 2-classification task:

5.25%: 6.71%

• 4-classification task:

10.92%: 14.47%

Chang and Jun 2019 [65]

Recognize the emotional responses of users towards given architectural design

Chang, Dong, and Jun Dataset [66]

GAN

Classification accuracy

 + 0.5%