# Electroencephalogram Emotion Recognition via AUC Maximization
## 2 Related Works  
Recent advancements in EEG-based emotion classification on the DEAP dataset have demonstrated significant progress, moving from simple traditional models to more complex deep learning models. Initially, studies utilized relatively straightforward approaches. For instance, [PERSON] et al. [24] employed a combination of wavelet energy, modified energy, wavelet entropy, and statistical features with SVMs, achieving accuracies of 84.95% for valence and 84.14% for arousal. Meanwhile, [PERSON] et al. [25] made contributions in feature engineering by using wavelet transforms and scalograms to structure multichannel neurophysiological signals. This method, combined with a hybrid deep learning model of CNNs and recurrent neural networks (RNNs), achieved accuracies of 72.06% for valence and 74.12% for arousal. As technology advanced, deep learning models such as deep neural networks (DNNs) [26; 27] and CNNs [28; 29] became more prevalent. [PERSON] et al. [30] utilized DNNs and CNNs to classify emotions from the DEAP dataset, achieving accuracies of 81.41% and 73.36% for valence and arousal, respectively. [PERSON] et al. [31] developed user-specific CNN models, achieving remarkable accuracies of 98.75% for valence and 97.58% for arousal in a three-class classification task. Some studies explored more complex deep learning architectures and optimization techniques [32; 33]. For instance, [PERSON] et al. [34] leveraged a Bimodal Deep AutoEncoder (BDAE), outperforming existing methods with an average accuracy of 83.25% on multimodal tasks using the DEAP dataset. [PERSON] et al. [35] introduced the Channel-Fused Dense Convolutional Network (CDCN), significantly improving accuracy to 92.24% for valence and 92.92% for arousal. [PERSON] et al. [36] applied CNN and multilayer perceptron (MLP) models, achieving F1-scores of 94.5% and 94% for arousal and valence classes, respectively. Additionally, [PERSON] et al. [37] developed an attention-based hybrid model, which achieved accuracies of 85.86% for arousal and 84.27% for valence. Meanwhile, [PERSON] et al. [38] introduced an Adaptive Neural Decision Tree (ANT), achieving an impressive 99.14% average accuracy in binary classification tasks. While AUC maximization has been successfully applied in various domains to address class imbalance, its application in EEG-based emotion recognition remains underexplored. This gap indicates an opportunity to investigate the effectiveness of AUC maximization methods within the context of EEG-based classification, particularly for imbalanced datasets like the DEAP dataset's 'liking' category. Our work aims to fill this gap by applying advanced AUC maximization algorithms to EEG emotion recognition and demonstrating their superiority over traditional methods in handling class imbalance. Comparison with prior works: compared with the extensive prior research on the DEAP dataset (see Table 1 for a limited reference), which predominantly focuses on the accuracy of models in the two main categories of arousal and valence, our work targets the 'liking' category. This category is notably characterized by an imbalanced data distribution (see Figure 1). Imbalance issues are observed in various domains, including cognitive science, where certain cognitive states may be less frequently represented, and in medical diagnosis, where certain diseases or conditions may be rare. For example, in medical imaging, datasets often contained a majority of healthy images with only a small fraction depicting abnormalities, leading to challenges in training robust models [39]. Despite the prevalence of such imbalances, many researchers continued to rely on machine learning methods trained on artificially balanced datasets. While this approach may have yielded high in-sample performance, it often resulted in poor out-of-sample generalization when the data distribution differed from the training set.