EEG-based emotion recognition (EER) has gained significant attention due to its potential for understanding and analyzing human emotions. While recent advancements in deep learning techniques have substantially improved EER, the field lacks a convincing benchmark and comprehensive open-source libraries. This absence complicates fair comparisons between models and creates reproducibility challenges for practitioners, which collectively hinder progress. To address these issues, we introduce LibEER, a comprehensive benchmark and algorithm library designed to facilitate fair comparisons in EER. LibEER carefully selects popular and powerful baselines, harmonizes key implementation details across methods, and provides a standardized codebase in PyTorch. By offering a consistent evaluation framework with standardized experimental settings, LibEER enables unbiased assessments of over ten representative deep learning models for EER across the four most widely used datasets. Additionally, we conduct a thorough, reproducible comparison of model performance and efficiency, providing valuable insights to guide researchers in the selection and design of EER models. Moreover, we make observations and in-depth analysis on the experiment results and identify current challenges in this community. We hope that our work will not only lower entry barriers for newcomers to EEG-based emotion recognition but also contribute to the standardization of research in this domain, fostering steady development. The library and source code are publicly available at https://github.com/XJTU-EEG/LibEER.

翻译：基于脑电图（EEG）的情绪识别（EER）因其在理解和分析人类情绪方面的潜力而受到广泛关注。尽管深度学习技术的最新进展已显著提升了EER的性能，但该领域仍缺乏一个令人信服的基准和全面的开源库。这种缺失使得模型间的公平比较变得复杂，并为实践者带来了可复现性挑战，共同阻碍了该领域的进展。为解决这些问题，我们提出了LibEER，这是一个旨在促进EER领域公平比较的综合性基准与算法库。LibEER精心选取了流行且强大的基线模型，统一了不同方法的关键实现细节，并提供了基于PyTorch的标准化代码库。通过提供具有标准化实验设置的一致性评估框架，LibEER能够对十余种具有代表性的EER深度学习模型在四个最广泛使用的数据集上进行无偏评估。此外，我们对模型性能与效率进行了全面且可复现的比较，为研究人员选择和设计EER模型提供了有价值的见解。更进一步，我们对实验结果进行了观察与深入分析，并指出了该领域当前面临的挑战。我们希望这项工作不仅能降低新进入者进入基于脑电的情绪识别领域的门槛，还能促进该领域研究的标准化，推动其稳步发展。本库及源代码已在 https://github.com/XJTU-EEG/LibEER 公开提供。