# Electroencephalogram Emotion Recognition via AUC Maximization

arXiv: https://arxiv.org/abs/2408.08979 · HTML: https://mlsanity.com/paper/2408.08979/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 16 Aug 2024.

## Builds on (most cited first, 13 of 13)

- [On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems](https://mlsanity.com/paper/1906.00331/) (2019)
- [A Unified Analysis of Extra-gradient and Optimistic Gradient Methods for Saddle Point Problems: Proximal Point Approach](https://mlsanity.com/paper/1901.08511/) (2019)
- [AUC Maximization in the Era of Big Data and AI: A Survey](https://mlsanity.com/paper/2203.15046/) (2022)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization](https://mlsanity.com/paper/2102.09468/) (2021)
- [Greedy Quasi-Newton Methods with Explicit Superlinear Convergence](https://mlsanity.com/paper/2002.00657/) (2020)
- [Recurrent Neural Network from Adder's Perspective: Carry-lookahead RNN](https://mlsanity.com/paper/2106.12901/) (2021)
- [AdaGDA: Faster Adaptive Gradient Descent Ascent Methods for Minimax Optimization](https://mlsanity.com/paper/2106.16101/) (2021)
- [Infrared Image Super-Resolution via Lightweight Information Split Network](https://mlsanity.com/paper/2405.10561/) (2024)
- [Emotion Recognition with Machine Learning Using EEG Signals](https://mlsanity.com/paper/1903.07272/) (2019)
- [Quasi-Newton Methods for Saddle Point Problems and Beyond](https://mlsanity.com/paper/2111.02708/) (2021)
- [Multiple Greedy Quasi-Newton Methods for Saddle Point Problems](https://mlsanity.com/paper/2408.00241/) (2024)
- [Inner speech recognition through electroencephalographic signals](https://mlsanity.com/paper/2210.06472/) (2022)

## Built on by (most cited first, 2 of 2)

- [Adaptive Self-Supervised Learning Strategies for Dynamic On-Device LLM Personalization](https://mlsanity.com/paper/2409.16973/) (2024)
- [Smoothing-Based Conformal Prediction for Balancing Efficiency and Interpretability](https://mlsanity.com/paper/2509.22529/) (2025)

## Abstract (arXiv)

Imbalanced datasets pose significant challenges in areas including neuroscience, cognitive science, and medical diagnostics, where accurately detecting minority classes is essential for robust model performance. This study addresses the issue of class imbalance, using the `Liking' label in the DEAP dataset as an example. Such imbalances are often overlooked by prior research, which typically focuses on the more balanced arousal and valence labels and predominantly uses accuracy metrics to measure model performance. To tackle this issue, we adopt numerical optimization techniques aimed at maximizing the area under the curve (AUC), thus enhancing the detection of underrepresented classes. Our approach, which begins with a linear classifier, is compared against traditional linear classifiers, including logistic regression and support vector machines (SVM). Our method significantly outperforms these models, increasing recall from 41.6% to 79.7% and improving the F1-score from 0.506 to 0.632. These results highlight the efficacy of AUC maximization via numerical optimization in managing imbalanced datasets, providing an effective solution for enhancing predictive accuracy in detecting minority but crucial classes in out-of-sample datasets.