Table 1 Reviewed papers that used GANs in motor imagery tasks

Abdelfattah et al., 2018 [26]

Enhance the model classification performance

PhysioNet

RGAN

Reconstruction

accuracy

Reconstruction:

• + 19.9% (w.r.t VAE)

• + 34.8% (w.r.t AE)

Classification accuracy

Classification (25% dataset):

• DNN + 36.1%

• SVM + 14.1%

• RFT + 13.1%

Classification (50% dataset):

• DNN + 39.1%

• SVM + 12.8%

• RFT + 12.6%

Hartmann et al., 2018 [27]

Achieve stabilization of the training

Private dataset

WGANs

• Classification accuracy

• IS

• FID

• ED

• SWD

• 91.25%

• 1.363

• 9.523

• − 0.056

• 0.078

Corley and Huang 2018 [28]

Produce high spatial resolution EEG data from low-resolution samples

Berlin BCI Competition III, Dataset V

WGANs

• Classification accuracy

Scale 2: 3.87 < HR

Scale 4: 5.75 < HR

• MSE

• MAE

Scale 2:

• − 37,497,940

• − 3885.34

Scale 4:

• − 72,991,320

• − 6385.61

Fahimi, Zhang et al., 2019 [29]

Enhance the model classification performance

A public dataset collected by [40]

DCGANs

Classification accuracy

NA

Fahimi, Dosen et al., 2021 [30]

Enhance the model classification performance

Private dataset + BCI competition III, Dataset IVa

DCGANs

Classification accuracy

• Diverted attention: + 7.32% (p < 0.01)

• Focused attention: + 5.45% (p < 0.01)

• IVa: + 3.57% (p < 0.02)

Li and Yu 2020 [32]

Enhance the model classification performance

BCI competition IV, Dataset 2b

cWGAN-GP

Classification accuracy

(w.r.t raw data)

• Shallow + 1.65%

• Deep4 + 2.89%

Debie et al., 2020 [33]

Protect EEG brain signals against illegal disclosure

BCI competition IV, Dataset 2a

GAN with differential privacy

Classification accuracy

NP-GAN (max:150 trial)

• SVM + 5.74%

• RF + 3.43%

• LDA + 6.39%

• LR + 9.54%

PP-GAN (max: 50 trial)

• SVM − 1.05%

• RF + 0.36%

• LDA − 0.19%

• LR + 0.18%

Zhang et al., 2020 [15]

Avoid overfitting and enhance the model classification performance

BCI competition IV (datasets 1

 + 2b)

CNN-DCGAN

Classification accuracy

• D1: + 8.7% (1:3)

• D2b: + 12.6% (1:3)

kappa value

• D1: + 0.1622%

• D2b: + 0.1981%

Luo et al., 2020 [35]

Reconstruct EEG signal with high sampling rates and sensitivity

Private dataset + 

Lucid et al., 2014 + 

BCI competition IV, dataset 2a

WGAN

 + 

(TSF-MSE) loss function

Classification accuracy

• MI: + 2.03%

• AO: + 4.1%

• GAL: + 4.11%

Reconstruction

accuracy

• MI: + 3.2%

• AO: + 4.5%

• GAL: + 5.38%

Yang et al., 2021 [38]

Address the challenge of insufficient MI data

Private dataset

 + 

BCI competition IV, Dataset 2a

cVAE- GAN

Classification accuracy

86.14%

D1 mean ~  + 4%

D2 mean ~  + 1.5%

• IS

• FID

• SWD

w.r.t Real

• − 0.121

•  + 11.364

•  + 0.067

Xie et al., 2021 [21]

Address the challenge of insufficient MI data

BCI competition IV, datasets 2a + 2b

LGANs (augmentation)

 + 

MoCNN (classification)

 + 

attention network

Classification accuracy

D1 (w.r.t raw data)

• LGAN + 8.23%

• Att-LAGN + 9.34%

D2 (w.r.t raw data)

• Att-LAGN: + 5.64% − 6.6%

Xu et al., 2021 [39]

Enhance the model classification performance

Private dataset

CycleGAN

Classification accuracy

 + 18.3%