无需手工特征，端到端分类多模态生理信号认知负荷
UniPhyNet: A Unified Network For Multimodal Physiological Raw Signal Classification
- 多尺度卷积+注意力机制聚焦关键特征
- 在CL-Drive数据集上准确率提升至80%（二分类）
- 适合实时认知状态监测场景应用
我们提出UniPhyNet，一种新型神经网络架构，用于基于多模态生理数据（即EEG、ECG和EDA信号）的认知负荷分类，无需显式提取手工特征。UniPhyNet融合多尺度并行卷积块与带通道注意力模块的ResNet型块，以聚焦信息特征；同时采用双向门控循环单元捕捉时序依赖。该架构通过学习到的特征图中间融合，在单模态与多模态配置下处理并整合信号。在CL-Drive数据集上，UniPhyNet将原始信号分类准确率从70%提升至80%（二分类），从62%提升至74%（三分类），优于基于特征的模型，证明其作为真实世界认知状态监测端到端解决方案的有效性。
原文摘要 · Abstract (English)
We present UniPhyNet, a novel neural network architecture to classify cognitive load using multimodal physiological data -- specifically EEG, ECG and EDA signals -- without the explicit need for extracting hand-crafted features. UniPhyNet integrates multiscale parallel convolutional blocks and ResNet-type blocks enhanced with channel block attention module to focus on the informative features while a bidirectional gated recurrent unit is used to capture temporal dependencies. This architecture processes and combines signals in both unimodal and multimodal configurations via intermediate fusion of learned feature maps. On the CL-Drive dataset, UniPhyNet improves raw signal classification accuracy from 70% to 80% (binary) and 62% to 74% (ternary), outperforming feature-based models, demonstrating its effectiveness as an end-to-end solution for real-world cognitive state monitoring.
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