Fusion-driven EEG reconstruction and cognitive workload recognition using conditional diffusion and graph-based learning

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.