.png){kind=icon}
返回
Fusion-driven EEG reconstruction and cognitive workload recognition using conditional diffusion and graph-based learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.aei.2025.104243.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cognitive workload recognition from EEG signals remains challenging due to real-world artifacts and missing data. To address this, we propose a unified reconstruction-classification framework that integrates EEG denoising and workload inference. Firstly, a Conditionally-Guided Denoising Diffusion Probabilistic Model (CG-DDPM) is introduced, which combines Gaussian noise modeling, a conditional encoder, and a Conditional Variational Autoencoder (CVAE) to guide a U-Net in removing diverse artifacts such as EMG, EOG, ECG, respiratory motion, powerline interference, and masked regions, while preserving essential neural activity. Secondly, an advanced classification network, EEG Graph Fusion Network (EEGGX-Net), is designed with a Hybrid Multi-Branch Encoder, a Bidirectional Multi-Head Cross Attention Fusion (MHCAF) module, and a Hierarchical Capsule Classifier (HCC) to jointly capture spatial, topological, and nonlinear dynamics of EEG signals. Both quantitative metrics (SNR: 16.50 dB, CC: 0.86, SC: 0.79) and topographic visualizations confirm CG-DDPM's efficacy in restoring meaningful neural activity. Using a strict subject-independent 5-fold cross-validation protocol on the STEW dataset, along with external validation on the iNCog-EEG dataset, the framework achieves state-of-the-art performance in both binary and ternary settings across raw, noisy, and reconstructed conditions, exceeding 98 % and 95 % accuracy, with narrow 95 % confidence intervals confirming statistical reliability. Comparative analyses also showed statistically significant performance gains, supported by p-value evaluations across models. Ablation studies and t-SNE visualizations reaffirm robustness and generalization. These results highlight the significant potential of this unified framework for real-time cognitive workload assessment in noise-prone environments such as neuroergonomics and human-automation systems.
Keyword:
Cognitive workload
Electroencephalography
Signal Reconstruction
Conditionally-Guided Denoising Diffusion
Probabilistic Model
Graph Fusion Network
期刊
IF:
9.9
论文数:
4.4K
被引数:
1.7W
机构
引用论文
Stress detection through wearable EEG technology: A signal-based approach通过可穿戴EEG技术进行压力检测：一种基于信号的方法
.png){kind=icon}
PREAI
分享收藏A multi-task engineering design intention recognition approach based on Vision Transformer and EEG data基于Vision Transformer和EEG数据的多任务工程设计意图识别方法
PREAI
分享收藏GMAEEG: A Self-Supervised Graph Masked Autoencoder for EEG Representation LearningGMAEEG: 一种用于EEG表示学习的自监督图掩蔽自动编码器
PREAI
分享收藏分享收藏
Using Semi-Supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance使用半监督域适应来增强基于EEG的跨任务精神工作量分类性能
PREAI
分享收藏Cognitive workload quantification for air traffic controllers: An ensemble semi-supervised learning approach空中交通管制员的认知工作量量化: 集成半监督学习方法
PREAI
分享收藏
查看更多内容