Diffusion models with graph attention for spatiotemporal EEG inpainting

Abstract

Contamination of artifacts, and loss of signal is a major impairment in Electroencephalography (EEG) recording reliability and standard methods of interpolation do not resolve the multichannel brain signal complex spatial-temporal dependencies. Current state-of-the-art approaches such as transformer or GAN based methods either do not consider EEG channels interaction or have high computational resources and cannot maintain critical frequency-domain properties. The synergistic combination of diffusion models with graph attention mechanisms and low-rank decomposition of the diffusion, which can both use the learned spatial relationships and specify mathematical priors, is still an open challenge in the field. In this paper, a new model that combines Denoising Diffusion Probabilistic Models (DDPM), Graph Attention Networks (GAT), and Singular Value Decomposition (SVD) to reconstruct corrupted EEG segments with 32 channels is suggested. The DEAP dataset (32 subjects, 40 emotion-elicitation trials) goes through preprocessing processes like baselines correction, channel-wise z-score normalization, percentile clipping, and 640-sample windowing with 50% overlap. SVD estimates first 16 orthogonal spatial-temporal components, which are used as priors and fused with raw features, and a hierarchical 1D U-Net with graph attention is then trained in 300 steps to reconstruct masked parts of the EEG. The DDPM+GAT+SVD model gives a Mean Squared Error (MSE) of 0.014153 ± 0.014911, Root Mean Squared Error (RMSE) of 0.110374 ± 0.044388, Mean Absolute Error (MAE) of 0.087305 ± 0.034363, Pearson Correlation Coefficient (PCC) of 0.8766 ± 0.1207 and Power Spectral Density (PSD) distance of 0.001293 ± 0.003648, ∼80.6% RMSE improvement over DEAP DIVE baseline. Ablation experiments bear out the synergistic contributions: DDPM+SVD (MSE 0.017276 ± 0.015000) and DDPM+GAT (MSE 0.026832 ± 0.020000) are worse by 22.1 percent and 47.2 percent, respectively, which confirms that mathematical decomposition, learned spatial attention and temporal diffusion are complements. Generalization without changes in architecture is proven by cross-dataset validation on PhysioNet Motor Imagery dataset (MSE 0.196824 ± 0.461074, RMSE 0.295641 ± 0.330788, PCC 0.8387 ± 0.1478). SVDs are also pre-computed in the process which saves training time which can be deployed with ease. These findings show that diffusion models structured hierarchically by incorporating graph attention and low-rank decomposition offer robust and physiologically plausible empirical findings on EEG reconstruction and are more effective in clinical diagnostic (as well as brain-computer interface and affective computing) tasks, compared to traditional artifact removal.