超越原始波形：融合EDA视觉表示用于压力检测
Beyond the Raw Waveform: Fusing Visual Representations of EDA for Stress Detection
- Honda Research Institute Japan（日本本田研究所）
- Hellenic Mediterranean University（希腊地中海大学）
- Ocean University of China（中国海洋大学）
- Deakin University（迪肯大学）
- National and Kapodistrian University of Athens（雅典国立卡波迪斯特里安大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究通过融合EDA的多种视觉表示（如PSD谱图等）并采用共享非对称注意力架构，在58人数据集上以70.97%的准确率超越原始波形（67.36%），证明视觉表示融合可提升压力检测性能。
AI中文摘要：
皮肤电活动（EDA）广泛应用于自动压力检测，然而大多数处理流程仅将其视为原始的一维波形。本研究探讨了EDA的互补视觉表示是否能提供对压力分类有用的信息，以及它们的融合是否能提升识别性能。从每次EDA记录中导出六种基于图像的表示：解缠绕的短时傅里叶变换（STFT）相位谱图、由该相位计算出的瞬时频率图、功率谱密度（PSD）谱图、连续小波变换尺度图、递归图以及渲染的波形轨迹。所选表示与原始波形一起，作为单通道多输入的各通道堆叠，并由共享的非对称注意力架构处理。在包含58名受试者的压力数据集上的实验表明，表示融合优于原始波形。最佳配置结合了五种表示（排除了解缠绕的相位谱图），达到了70.97%的测试准确率，而原始波形为67.36%。单独的PSD谱图达到了69.44%，以较低的计算成本接近最佳融合配置。结果表明，同一EDA信号的替代视觉形式可以为压力检测提供有用的归纳偏置，且紧凑选择的互补表示可能比单独使用原始波形更有效。
英文摘要：
Electrodermal activity (EDA) is widely used in automatic stress detection, yet most pipelines treat it only as a raw one-dimensional waveform. This study examines whether complementary visual representations of EDA provide useful information for stress classification and whether their fusion im- proves recognition performance. Six image-based representations are derived from each EDA recording: an unwrapped short-time Fourier transform (STFT) phase spectrogram, an instantaneous-frequency map computed from that phase, a power spectral density (PSD) spectrogram, a continuous wavelet transform scalogram, a recurrence plot, and a rendered waveform trace. The selected representations are stacked as channels of a single multichannel input, together with the raw waveform, and processed by a shared asymmetric-attention architecture. Experiments on a 58-subject stress dataset show that representation fusion improves over the raw waveform. The best configuration, which combines five representations while excluding the unwrapped phase spectrogram, reaches 70.97% test accuracy, compared with 67.36% for the raw waveform. The single PSD spectrogram achieves 69.44%, remaining close to the best-fused configuration at a lower computational cost. The results show that alternative visual forms of the same EDA signal can provide useful inductive biases for stress detection, and that a compact selection of complementary representations can be more effective than the raw waveform alone.
