Analysis of Connectivity Model to Study the Neurophysiological Process for Autism Detection

Studying network communication within the neurons is the next step toward exploring the complexities of the human brain. In this research, we used electroencephalography (EEG) to study the neurophysiological processes because of their high temporal and spatial resolution. EEG stands out to be a vital modality in assessing patients with brain abnormalities like Autism, Epilepsy, Dementia, and Parkinson's. Nowadays, a large number of children worldwide is affected by Autism Spectrum Disorder, which impairs the ability to communicate and behave. In this research, we generate the connectivity models using the EEG signal dataset of Autism and normal children. Here, connectivity models are presented into a graphical form using different measures like phase synchronization, classical measures, granger causality, and information theory. These parameters were used to analyze the variation between Autistic and typically developed children.