EEG-based emotion recognition (EER) is garnering increasing attention due to its potential in understanding and analyzing human emotions. Recently, significant advancements have been achieved using various deep learning-based techniques to address the EER problem. However, the absence of a convincing benchmark and open-source codebase complicates fair comparisons between different models and poses reproducibility challenges for practitioners. These issues considerably impede progress in this field. In light of this, we propose a comprehensive benchmark and algorithm library (LibEER) for fair comparisons in EER by making most of the implementation details of different methods consistent and using the same single codebase in PyTorch. In response to these challenges, we propose LibEER, a comprehensive benchmark and algorithm library for fair comparisons in EER, by ensuring consistency in the implementation details of various methods and utilizing a single codebase in PyTorch. LibEER establishes a unified evaluation framework with standardized experimental settings, enabling unbiased evaluations of over ten representative deep learning-based EER models across the four most commonly used datasets. Additionally, we conduct an exhaustive and reproducible comparison of the performance and efficiency of popular models, providing valuable insights for researchers in selecting and designing EER models. We aspire for our work to not only lower the barriers for beginners entering the field of EEG-based emotion recognition but also promote the standardization of research in this domain, thereby fostering steady development. The source code is available at \url{https://github.com/ButterSen/LibEER}.

翻译：基于脑电图的情绪识别因其在理解和分析人类情绪方面的潜力而受到越来越多的关注。近年来，利用各种基于深度学习的技术解决EER问题取得了显著进展。然而，由于缺乏令人信服的基准和开源代码库，不同模型之间的公平比较变得复杂，也给实践者带来了可复现性挑战。这些问题严重阻碍了该领域的进展。鉴于此，我们提出了一个全面的基准和算法库（LibEER），通过使不同方法的大部分实现细节保持一致并使用PyTorch中的单一代码库，以实现EER领域的公平比较。LibEER建立了一个具有标准化实验设置的统一评估框架，能够在四个最常用的数据集上对十多种代表性的基于深度学习的EER模型进行无偏评估。此外，我们对流行模型的性能和效率进行了详尽且可复现的比较，为研究人员选择和设计EER模型提供了宝贵的见解。我们希望我们的工作不仅能降低初学者进入基于脑电图的情绪识别领域的门槛，还能促进该领域研究的标准化，从而推动其稳步发展。源代码可在 \url{https://github.com/ButterSen/LibEER} 获取。