## 2 Materials and Methods
### Feature Extraction  
Differential entropy (DE) is often used as a measure of the complexity of continuous random variables and has been demonstrated to be suitable for EEG emotion recognition studies [22]. The standard definition of DE is as Equation (1):  
\[DE(X)=-\int_{X}f(x)\log(f(x))dx \tag{1}\]  
where \(X\) represents the EEG sequence and \(f(x)\) represents its probability density function. Given that \(X\) follows a Gaussian distribution \(N(\mu,\sigma^{2})\), the DE can be further represented as Equation (2):  
\[DE(X)=-\int_{-\infty}^{\infty}\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp\frac{\left(x -\mu\right)^{2}}{2\sigma^{2}}\log(\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp\frac{ \left(x-\mu\right)^{2}}{2\sigma^{2}})dx=\frac{1}{2}\log 2\pi e\sigma^{2} \tag{2}\]  
For EEG samples of \(T\) seconds in length, an \(N\times C\times T\) dimensional DE feature matrix can be extracted using a 1 s non-overlapping time window, where \(N\) denotes the number of EEG channels and \(C\) is the number of EEG frequency bands.  
Following the international 10-20 system, this paper divides the EEG signal channels of the DEAP dataset into 4 different brain regions. Specifically, the frontal lobe (Fp1, Fp2, F3, F4, Fz, AF3, AF4), temporal lobe (F7, T7, F7, FCS, CP5, F8, T8, F8, FC6, CP6), parietal lobe (P3, P4, Pz, C3, C4, Cz, CP1, CP2, FC1, FC2), and occipital lobe (O1, O2, Oz, PO3, PO4). For each subject, the EEG signal was initially decomposed into 4 frequency bands (\(\theta\): 4-7 Hz, \(\alpha\): 8-13 Hz, \(\beta\): 14-30 Hz, \(\gamma\): 31-45 Hz). Subsequently, using 1 s non-overlapping time windows on signal channels corresponding to each brain region, DE features are extracted for the four frequency bands. Therefore, after the brain region division and feature extraction, each subject consists of 4 feature matrices with shapes (40, \(k\), 4, 60). Where 40 denotes the number of trails, \(k\) denotes the number of electrode channels contained in different brain regions (frontal lobe contains 7 electrodes, parietal lobe contains 10 electrodes, temporal lobe contains 10 electrodes, and occipital lobe contains 5 electrodes), 4 denotes the number of frequency bands, and 60 denotes the number of DE features on each frequency band. After data transformation, the shape of the feature matrix is converted to (2400, 4, \(k\)), which corresponds to 2400 labels in the valence, arousal, dominance, and liking dimensions, respectively. It is evident that for each specific brain region, there are 2400 samples, each composed of DE features from 4 frequency bands, and each frequency band contains  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Array Name** & **Array Shape** & **Array Contents** \\ \hline Data & 40 \(\times\) 40 \(\times\) 8064 & Video/trail \(\times\) channel \(\times\) data \\ \hline Label & 40 \(\times\) 4 & Video/trail \(\times\) label (valence, arousal, dominance, liking) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The data format for each subject in the DEAP dataset.  
DE features. Furthermore, if no brain region division is performed, each subject contains 1 feature matrix with a shape of (2400, 4, 32), where \(k\) represents the 32 EEG signal channels covering the entire brain.