# Electroencephalogram Emotion Recognition via AUC Maximization
## 3 Dataset
### Overview  
The DEAP dataset was widely used in studying the relationship between physiological signals and emotional states. The dataset consisted of data from 32 participants who each watched 40 one-minute video clips, selected from an initial pool of 120 based on subjective emotional assessments. EEG signals were recorded using a Biosemi ActiveTwo system with 32 AgCl electrodes, following the international 10-20 system. Each participant's EEG data were captured across 40 channels, originally sampled at 512 Hz and later downsampled to 128 Hz. The data also underwent preprocessing, including the removal of Electrooculography (EOG) artifacts and band-pass filtering between 4.0 and 45.0 Hz. Additionally, each \begin{table}
\begin{tabular}{c c c} \hline \hline
**Article** & **Existing Methods** & **Results** \\ \hline
[44] & Log-Euclidean Riemannian & Accuracy 72.6\% for arousal \\  & Metric & Accuracy 74.6\% for valence \\ \hline
[45] & Spiking Neural Networks & Accuracy (arousal, 74\%) \\  & (SNNs) & Accuracy (dolinance, 78\%) \\  & & Accuracy (liking, 86.27\%) \\ \hline
[35] & Channel-Fused Dense & Accuracy 92.24\% for valence \\  & Convolutional Network & Accuracy 92.92\% for arousal \\  & (CDCN) & Accuracy 92.92\% for arousal \\ \hline
[46] & LSTM with Higher-Order & Accuracy 82.01\% \\ \hline
[31] & CNN & Around 98\% accuracy for 3-class arousal and valence \\ \hline
[30] & CNN & Accuracy 81.41\% for valence \\  & & Accuracy 73.36\% for arousal \\ \hline
[25] & C-RNN & Accuracy 72.06\% for valence \\  & & Accuracy 74.12\% for arousal \\ \hline
[34] & MDL & Accuracy 85.2\% for valence \\  & & Accuracy 80.5\% for arousal \\ \hline
[24] & WT-SVM & Accuracy 84.95\% for valence \\  & & Accuracy 84.14\% for arousal \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Feature** & **Sliding?** & **Description** \\ \hline F & PSD & ✓ & Power Spectral Density, representing power distribution over frequency bands \\ \hline T & PLV & ✓ & Phase Locking Value, measuring phase synchronization between channels \\  & Corr & ✓ & Correlation between time-series segments across channels \\ \hline \multirow{5}{*}{FT} & DE & ✓ & Differential Entropy, measuring signal complexity \\  & Segment Range & ✓ & Range of values within a segment, capturing signal variability \\ \cline{1-1}  & Segment Moments & ✓ & Statistical moments (mean, variance, etc.) of the segment \\ \cline{1-1}  & Segment Diff & ✓ & Difference between two consecutive segments \\ \cline{1-1}  & Channel Range & ✗ & Range of values of channels \\ \cline{1-1}  & Channel Moments & ✗ & Statistical moments (mean, variance, argmin, argmax, etc.) of channels \\ \hline \hline \end{tabular}
\end{table} 
Table 2: A summary of feature extraction methods for EEG signals. F represents frequency domain features, T represents time domain features, and FT represents frequency–time domain features. video trial included a 3 s pre-trial baseline, with the total recording per trial amounting to 63 s.  
The dataset included four primary emotion labels: arousal, valence, dominance, and liking. Among these labels, the arousal and valence categories were commonly used due to their relatively balanced distributions, facilitating effective model evaluation in machine learning and deep learning applications. However, the 'liking' label was notably imbalanced (see Figure 1), with approximately one-third of scores below 5 and two-thirds above, presenting challenges for accurate model assessment. This kind of label imbalance is typical in neuroscience and medical datasets, such as those related to epilepsy, dementia, Parkinson's disease, and depression [49; 50; 51]. A summary of the DEAP dataset and the channels utilized in our study is presented in Table 3. Given the imbalanced nature of certain features and their significance in detecting diseases, particularly those with fewer samples, it was crucial to develop methods that could effectively identify these cases within large datasets.