# EEG Data Augmentation for Emotion Recognition with a Task-Driven GAN
## 3 The Proposed Method
### EEG Data Augmentation for Emotion Recognition
#### 3.2.2 The Task-Driven CWGAN
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classifiers**} & \multirow{2}{*}{**Emotional Dimensions**} & **Arousal** & \multicolumn{4}{c}{**Valence**} \\ \cline{2-7}  & & **Original** & **CWGAN** & **CWGAN-T** & **Original** & **CWGAN** & **CWGAN-T** \\ \hline
**SVM** & 0.8485 & 0.8589 & 0.9135 & 0.8330 & 0.8545 & 0.9064 \\ \hline
**Tasknet** & 0.8015 & 0.8876 & 0.9022 & 0.7880 & 0.8755 & 0.8956 \\ \hline
**CNN-noPooling** & 0.8499 & 0.9054 & 0.9225 & 0.8346 & 0.8977 & 0.9187 \\ \hline
**Lenet-with-BN** & 0.8748 & 0.9050 & 0.9352 & 0.8664 & 0.9021 & 0.9275 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The mean accuracy of emotion recognition without data augmentation, with data augmentation through the original CWGAN, and with data augmentation through the proposed method (SVM: support vector machine is often used for emotion recognition of physiological signals; Tasknet: the classifier in the proposed structure; CNN-noPooling: the 4-layer convolutional neural network proposed in related research [16] on EEG emotion recognition; Lenet5 with BN: Lenet5 with BatchNorm layers). ## References

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