将脑电情绪信号转为匿名表情包，让大脑情绪可直观看见
See the Emotion: A Facial Emoji Proxy Modeling for EEG Emotion Recognition
- 用面部表情包建模脑电信号，实现神经活动的可视化
- 在EAV和MMER数据集上达到当前最优准确率
- 生成的动画既真实又保护隐私，适合人机交互研究
尽管基于脑电的情绪识别已有较高准确率，现有模型仍为难以理解的“黑箱”，缺乏抽象神经特征与人类可理解状态之间的语义关联。本文将脑电可解释性重构为跨模态生成任务，从特征归因转向行为可视化。提出面部表情包代理建模（Facial Emoji Proxy Modeling），将高维脑电信号转化为身份匿名的面部表情包。基于神经-面部关联的神经科学启发，该方法将神经表征锚定在可观测的面部动态流形中。技术上，框架融合了专用于表达相关空间协同的FMENet主干网络，以及将表情包重建作为结构化语义正则器的面部表情学习分支（FELB）。在EAV和MMER基准上的大量实验表明，该方法在仅使用脑电的模型中达到最先进性能。关键优势在于生成语义忠实的面部动画，为大脑情绪演变提供透明、隐私保护的可视化窗口，真正实现‘看见情绪’。代码已公开于https://github.com/xian-sh/SeeEmotion。
原文摘要 · Abstract (English)
Despite the high accuracy of EEG-based emotion recognition, existing models remain opaque "black boxes", lacking semantic grounding between abstract neural features and human-interpretable states. In this paper, we reframe EEG explainability as a cross-modal generation task, shifting the paradigm from feature attribution to behavioral visualization. We introduce Facial Emoji Proxy Modeling, a novel framework that translates high-dimensional EEG signals into identity-anonymized facial emojis. Guided by the neuroscientific inspiration of neural-facial association, this approach grounds neural representations in the manifold of observable facial dynamics. Technically, our framework integrates FMENet, a specialized backbone modeling expression-relevant spatial synergies, and the Facial Emoji Learning Branch (FELB), which treats emoji reconstruction as a structured semantic regularizer. Extensive experiments on EAV and MMER benchmarks demonstrate that our method achieves state-of-the-art accuracy among EEG-only models. Crucially, it generates semantically faithful facial animations that provide a transparent, privacy-preserving window into the brain's emotional evolution, effectively allowing users to "see the emotion" directly from neural signals. Code is available at https://github.com/xian-sh/SeeEmotion
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