# Electroencephalogram Emotion Recognition via AUC Maximization
## 1 Introduction  
Electroencephalography (EEG) plays a critical role in the study of human brain function [1], offering valuable insights into cognitive [2], emotional [3], and motor processes [4]. The ability to measure the brain's electrical activity in real time with high temporal resolution makes EEG an indispensable tool in neuroscience and clinical diagnostics. Its application extends from understanding basic cognitive functions to complex tasks like emotion recognition, where EEG provides a direct window into the brain's response to various stimuli. Unlike other physiological signals, EEG is highly sensitive to the brain's intrinsic activities, offering a unique perspective on neural dynamics that cannot be easily captured through other modalities [5, 6]. In recent years, EEG-based emotion recognition has gained considerable attention, particularly in the fields of brain-computer interfacing and human-machine interaction [7, 8, 9]. Various methods have been developed to decode emotional states from EEG signals, leveraging the rich temporal and spatial information embedded in these signals [10]. Traditional machine learning approaches, such as support vector machines (SVMs) and k-nearest neighbors (KNN), have been widely used, often with features like wavelet transforms and power spectral density [11, 12]. More recently, deep learning models, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, have shown promise in improving classification accuracy by automatically extracting complexfeatures from raw EEG data [13; 14]. These advancements highlight the potential of EEG in emotion recognition. Despite these advancements, a significant challenge in EEG-based emotion recognition has been the issue of class imbalance [15]. In many EEG datasets, the distribution of emotional states is skewed, with some emotions being underrepresented. This imbalance could lead to biased classifiers that perform well on majority classes but fail to accurately detect minority classes. Various strategies have been developed to address this issue, including data augmentation techniques like the synthetic minority over-sampling technique (SMOTE) and generative adversarial network-based approaches [16; 17]. These methods aim to artificially balance the dataset by generating synthetic samples for the minority classes, thereby improving the model's ability to generalize across different emotional states. However, these solutions often involve complex preprocessing steps and could introduce noise, potentially affecting the overall model performance. AUC maximization methods have emerged as effective approaches for handling class imbalance in machine learning [18; 19]. Unlike traditional loss functions that focus on minimizing classification errors, AUC maximization directly optimizes the area Under the receiver operating characteristic (ROC) curve, which is a threshold-independent metric reflecting the model's ability to distinguish between classes. Recent advancements in this area include the development of efficient stochastic optimization algorithms [20; 21] and deep learning frameworks that integrate AUC maximization into neural network training [22; 23]. These methods have demonstrated significant improvements in various domains, particularly in dealing with imbalanced datasets where minority class detection is critical. In this paper, we aim to fill a gap in the literature by applying AUC maximization methods to EEG-based emotion recognition tasks. To the best of our knowledge, AUC maximization has not been extensively explored in this context. By focusing on maximizing the AUC with a simple linear classifier, we demonstrate that it is possible to directly address the imbalanced nature of EEG datasets without the need for complex preprocessing or synthetic data generation. Our approach not only simplifies the model training process but also enhances the detection of minority classes, leading to more robust and reliable emotion recognition systems.