用傅里叶与图结构结合提升脑电情绪识别准确率
A novel Fourier Adjacency Transformer for advanced EEG emotion recognition
- 将傅里叶周期分析与通道间关联建模融合，提取脑电信号周期特征
- 在SEED和DEAP数据集上准确率提升约6.5%，优于现有方法
- 适合研究脑机接口、情绪计算的学者参考
脑电情绪识别面临噪声干扰、信号非平稳性及大脑活动复杂性的挑战，难以实现精准分类。本文提出傅里叶邻接注意力网络（Fourier Adjacency Transformer），通过傅里叶启发模块提取嵌入脑电信号中的周期性特征，有效分离非周期成分；再采用邻接注意力机制强化跨通道的通用相关模式，并将其与样本级特征结合。在SEED和DEAP数据集上的实证评估表明，该方法显著优于现有先进模型，识别准确率提升约6.5%。通过统一周期性与结构化信息，为脑电情绪分析提供了新方向。
原文摘要 · Abstract (English)
EEG emotion recognition faces significant hurdles due to noise interference, signal nonstationarity, and the inherent complexity of brain activity which make accurately emotion classification. In this study, we present the Fourier Adjacency Transformer, a novel framework that seamlessly integrates Fourier-based periodic analysis with graph-driven structural modeling. Our method first leverages novel Fourier-inspired modules to extract periodic features from embedded EEG signals, effectively decoupling them from aperiodic components. Subsequently, we employ an adjacency attention scheme to reinforce universal inter-channel correlation patterns, coupling these patterns with their sample-based counterparts. Empirical evaluations on SEED and DEAP datasets demonstrate that our method surpasses existing state-of-the-art techniques, achieving an improvement of approximately 6.5% in recognition accuracy. By unifying periodicity and structural insights, this framework offers a promising direction for future research in EEG emotion analysis.
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