A Hybrid Tensor Factorization - Singular Spectrum Analysis Approach for ERP-based Assessment of Autism in Children

Abstract

Diagnosis of autism spectrum disorder (ASD) in children is often achieved by estimating the amplitudes and latencies of visual event-related potentials (ERPs). This requires accurate detection of desired ERPs, in our case P1 and N170, which are sensitive to visual stimuli. We aim to develop a hybrid of tensor factorization (TF) and singular spectrum analysis (SSA) to detect these components from electroencephalograms (EEGs) and restore the inherent noise and artifacts. The application of single-channel SSA to the detected sources by TF results in the removal of brain beta activity considerably enhancing the accuracy. The ERP parameters (amplitudes and latencies) are automatically estimated and applied to a decision-tree classifier leading to 100% accuracy.