Recurrence Quantification Analysis of EEG signals for Children with ASD

Abstract

The present study aims at identifying the brain response for auditory/visual stimuli in typically developing (TD) and children with autism through electroencephalography (EEG). Early diagnoses do help in customized training and progressing the children in regular stream. To reveal the underlying brain dynamics, non-linear analysis was employed. In the current study, Recurrent Quantification Analysis (RQA) with varying parameters was analyzed. For better information retrieval, cosine distance metric is additionally considered for analysis and compared with other distance metrics in RQA. Each computational combination of RQA is measured and the responding channels were analyzed and discussed. It was observed that the FAN neighborhood with cosine distance parameters was able to discriminate between ASD and TD prominently.