Identifying Autism from EEG Signals Using Features Derived from Active Brain Source Models

Abstract

Autism is a neurological condition that influences brain function and behavior, often becoming evident in early childhood and lasting into adulthood. It is defined by challenges in social interaction, communication, and behavior, as well as a decreased attention to the surrounding environmentEarly identification and diagnosis of autism can play a crucial role in addressing its impacts and enhancing social and communication abilities. Various tools, like questionnaires and neurological techniques, are used for this purpose. One such technique is electroencephalography (EEG), which records the brain's electrical activity through sensors positioned on the scalp. This paper introduces a method to identify autism using EEG data. The process starts by pinpointing active brain sources through localization techniques, followed by the application of a dual Kalman filter to assess their activity. Features are subsequently derived from EEG signals using multivariate autoregressive moving average (MVARMA) and multivariate integrated autoregressive (ARIMA) models. Principal component analysis (PCA) is employed to identify essential features, and a K-nearest neighbor (KNN) classifier is utilized to classify individuals as either autistic or neurotypical. The proposed approach achieves higher accuracy and superior classification performance compared to existing methods, highlighting its effectiveness in identifying autism.