# EEG Emotion Classification Based on Graph Convolutional Network
## 4 Experiments and Discussions
### Feature Visualization  
In this study, we attempt four different types of input features that are often used in EEG-based emotion recognition:  
* Raw signal.
* Differential entropy (\(DE\)) is a measure of the complexity of a continuous random variable. It can be calculated as following: \[DE(X) = -\int_{-\infty}^{+\infty}\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac {(x-y)^{2}}{2\sigma^{2}}}\] (20) \[\cdot\log\left(\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{(x-y)^{2} }{2\sigma^{2}}}\right)\] \[= \frac{1}{2}\log(2\pi e\sigma^{2}),\] where \(\mu\) and \(\sigma\) denote the mean and standard deviation.
* Fast Fourier transform (FFT) is an algorithm that is used to compute the discrete Fourier transform(DFT) of a sequence of data points in an efficient way. It can be defined as: \[S(f)=\int_{-\infty}^{+\infty}s(t)e^{-j2\pi ft}dt,\] (21) where \(f\) is the frequency; \(t\) denotes time; \(s(t)\) is the signal in time domain.
* Power spectral density (\(PSD\)) refers to the distribution of power of different frequencies in a signal. It is calculated by taking the squared magnitude of Fourier transform of the signal: \[PSD(f)=\bigg{|}\int_{-\infty}^{+\infty}x(\tau)e^{-j2\pi f\tau}d\tau\bigg{|}^{2}.\] (22)  
Note that for the frequency-domain features, we decompose the EEG signal into five frequency bands: \(\delta\) band (1-3 Hz), \(\theta\) band (4-7 Hz), \(\alpha\) band (8-12 Hz), \(\beta\) band (8-12 Hz) and \(\gamma\) band (8-12 Hz). The features are extracted from each band respectively, which indicates the dimension of features for each segment would be five.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Region No.** & **MLP** & **CNN** & **GCN** \\ \hline  & & DEAP & \\ \hline
1. & 94.11 & 93.58 & 94.13 \\
2. & 97.48 & 97.64 & 98.65 \\
3. & 97.05 & 97.54 & 98.01 \\ \hline  & & Dreamer & \\ \hline
1. & 94.25 & 94.00 & 94.29 \\
2. & 99.12 & 99.09 & 99.15 \\
3. & 97.11 & 96.94 & 97.03 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy (%) of region division strategies combined with different region encoders. The first column is the index of the region division strategy.  
To compare learning of models with different input features and region encoders, we visualize the features of the last fully connected layers using the t-SNE visualization tool. The features of data from the same subject are extracted to make a fair comparison. The t-SNE visualization of DEAP is shown in Figure 5. With DE, PSD and FFT as input features, MLP region encoders are unable to distinguish between the two classes, while CNN and GCN have relatively better performance. All the region encoders are able to observe distinct separation with raw signal input.  
The features from the Dreamer dataset, as shown in Figure 6, are more variable between low/high classes. We are able to observe that the PSD and FFT features have relatively more outliers for all region encoders. On the other hand, a more distinct boundary can be detected with other features, especially DE.  
The differences between observations of DEAP and Dreamer might be attributed to their intrinsic differences as datasets, e.g., stimuli they used or the collection of data and experiment settings. Based on the analysis of features, for the following experiments, we choose the raw signal to be the input for the DEAP dataset and DE as the input for the Dreamer dataset.