A self-attention imputation model combined with a convolutional autoencoder refinement fills missing segments in smooth pursuit eye movements more accurately than PCHIP, SSA, and KNN, especially for long gaps.
Direct and indirect effects of attention and visual function on gait impairment in parkinson’s disease: influence of task and turning
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Imputation of Missing Data in Smooth Pursuit Eye Movements Using a Self-Attention-based Deep Learning Approach
A self-attention imputation model combined with a convolutional autoencoder refinement fills missing segments in smooth pursuit eye movements more accurately than PCHIP, SSA, and KNN, especially for long gaps.