标题： 脑中情感的映射：一种可解释深度学习的双半球神经模型 显示英文标题

标题： Mapping Emotions in the Brain: A Bi-Hemispheric Neural Model with Explainable Deep Learning

摘要： 最近的进展表明，通过采用包含神经科学先验的双半球神经架构，从脑电图（EEG）信号中进行情感识别显示出前景。然而，在诸如情感计算和认知建模等敏感领域中，可解释性仍然是其应用的重大限制。在本工作中，我们引入了一个针对双流EEG分类器的后处理可解释性框架，扩展了局部可解释模型无关解释（LIME）方法，以适应结构化的双半球输入。我们的方法适应LIME以处理对应于左半球和右半球EEG通道组的结构化双分支输入。它将预测相关性分解为各半球和情感类别的每通道贡献。我们将此框架应用于一个之前经过验证的双分支循环神经网络，该网络在EmoNeuroDB数据集上进行训练，该数据集是在基于VR的情感诱发任务中采集的EEG记录。所产生的解释揭示了与已知的神经生理现象一致的情感特异性半球激活模式，例如快乐时的额叶偏侧化和悲伤时的后部不对称性。此外，我们对样本之间的局部解释进行聚合，以获得全局通道重要性轮廓，从而实现对模型决策的神经生理学基础解释。对对称电极之间的相关性分析进一步突显了模型的情感依赖性偏侧化行为，支持了情感神经科学中报告的功能不对称性。 显示英文摘要

摘要： Recent advances have shown promise in emotion recognition from electroencephalogram (EEG) signals by employing bi-hemispheric neural architectures that incorporate neuroscientific priors into deep learning models. However, interpretability remains a significant limitation for their application in sensitive fields such as affective computing and cognitive modeling. In this work, we introduce a post-hoc interpretability framework tailored to dual-stream EEG classifiers, extending the Local Interpretable Model-Agnostic Explanations (LIME) approach to accommodate structured, bi-hemispheric inputs. Our method adapts LIME to handle structured two-branch inputs corresponding to left and right-hemisphere EEG channel groups. It decomposes prediction relevance into per-channel contributions across hemispheres and emotional classes. We apply this framework to a previously validated dual-branch recurrent neural network trained on EmoNeuroDB, a dataset of EEG recordings captured during a VR-based emotion elicitation task. The resulting explanations reveal emotion-specific hemispheric activation patterns consistent with known neurophysiological phenomena, such as frontal lateralization in joy and posterior asymmetry in sadness. Furthermore, we aggregate local explanations across samples to derive global channel importance profiles, enabling a neurophysiologically grounded interpretation of the model's decisions. Correlation analysis between symmetric electrodes further highlights the model's emotion-dependent lateralization behavior, supporting the functional asymmetries reported in affective neuroscience.