构建首个开源的脑电情绪识别评估框架，支持可复现实验
LibEMER: A novel benchmark and algorithms library for EEG-based Multimodal Emotion Recognition
- 提供统一框架与完整PyTorch实现，支持多种深度学习模型
- 在3个公开数据集上标准化评估，覆盖2类学习任务
- 解决方法不可复现、评测不透明难题，适合情绪计算研究者
基于脑电的情绪识别（EMER）近年来取得显著进展，但受制于人类神经系统的内在复杂性，仍面临三大挑战：(i) 缺乏开源实现；(ii) 无标准化、透明的基准用于公平性能评估；(iii) 对核心挑战与未来方向的深入讨论严重不足。为此，我们提出LibEMER，一个统一的评估框架，包含精选深度学习方法的全可复现PyTorch实现，并提供标准化的数据预处理、模型实现与实验设置协议。该框架可在三个广泛使用的公共数据集上对两类学习任务进行无偏性能评估。开源库已公开：https://anonymous.4open.science/r/2025ULUIUBUEUMUEUR485384
原文摘要 · Abstract (English)
EEG-based multimodal emotion recognition(EMER) has gained significant attention and witnessed notable advancements, the inherent complexity of human neural systems has motivated substantial efforts toward multimodal approaches. However, this field currently suffers from three critical limitations: (i) the absence of open-source implementations. (ii) the lack of standardized and transparent benchmarks for fair performance analysis. (iii) in-depth discussion regarding main challenges and promising research directions is a notable scarcity. To address these challenges, we introduce LibEMER, a unified evaluation framework that provides fully reproducible PyTorch implementations of curated deep learning methods alongside standardized protocols for data preprocessing, model realization, and experimental setups. This framework enables unbiased performance assessment on three widely-used public datasets across two learning tasks. The open-source library is publicly accessible at: https://anonymous.4open.science/r/2025ULUIUBUEUMUEUR485384
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