自适应时间动态用于个性化情绪识别：一种液态神经网络方法
Adaptive Temporal Dynamics for Personalized Emotion Recognition: A Liquid Neural Network Approach
AI总结：
本文提出了一种基于液态神经网络的多模态框架，用于提升基于EEG的情绪识别准确率，通过结合卷积特征提取、可学习时间常数和注意力引导融合，实现高准确率和可解释性。
摘要：
从生理信号中识别情绪仍然具有挑战性，因为这些信号具有非平稳、噪声和受试者依赖的特性。本文首次全面应用液态神经网络进行基于EEG的情绪识别。所提出的多模态框架结合了卷积特征提取、具有可学习时间常数的液态神经网络以及注意力引导融合，以建模互补的外周生理和人格特征的时序EEG动态。专用子网络用于处理EEG特征和辅助模态，共享的自编码器基于融合模块用于在分类前学习判别潜在表示。在PhyMER数据集上针对七种情绪类别的受试者依赖实验实现了95.45%的准确率，超过了之前报告的结果。此外，时间注意力分析提供了可解释的情绪特定时间相关性见解，t-SNE可视化显示了增强的类别分离性，突显了所提出方法的有效性。最后，对时间动态的统计分析证实，网络自组织成具有专门快速和慢速神经元的功能组，证明了网络能够独立调节可学习的时间常数和记忆主导性，以有效捕捉复杂的情绪特征。
英文摘要：
Emotion recognition from physiological signals remains challenging due to their non-stationary, noisy, and subject-dependent characteristics. This work presents, to the best of our knowledge, the first comprehensive application of liquid neural networks for EEG-based emotion recognition. The proposed multimodal framework combines convolutional feature extraction, liquid neural networks with learnable time constants, and attention-guided fusion to model temporal EEG dynamics with complementary peripheral physiological and personality features. Dedicated subnetworks are used to process EEG features and auxiliary modalities, and a shared autoencoder-based fusion module is used to learn discriminative latent representations before classification. Subject-dependent experiments conducted on the PhyMER dataset across seven emotional classes achieve an accuracy of 95.45%, surpassing previously reported results. Furthermore, temporal attention analysis provides interpretable insights into emotion-specific temporal relevance, and t-SNE visualizations demonstrate enhanced class separability, highlighting the effectiveness of the proposed approach. Finally, statistical analysis of temporal dynamics confirms that the network self-organizes into distinct functional groups with specialized fast and slow neurons, proving it independently tunes learnable time constants and memory dominance to effectively capture complex emotion artifacts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
