Autistic recognition from EEG signals by extracted features from several time series models

Abstract

Autism is a neurological and psychological disorder that typically manifests in childhood and persists into adulthood. It is characterized by atypical social, behavioral, and communication skills, as well as diminished attention to the surrounding environment. The detection and recognition of autism can contribute to the prevention of its development and the enhancement of social and communicational abilities. Various methods are employed for autism recognition, including questionnaire tests and neurological techniques. One such neuroimaging method is electroencephalography (EEG), which records the brain's electrical activities through sensors placed on the scalp. This paper proposes a method for identifying individuals with autism using EEG signals and features extracted from a multivariate autoregressive moving average (MVARMA) and multivariate integrated autoregressive(ARIMA) models. The approach begins by estimating active sources through source localization methods, followed by the application of a dual Kalman filter to estimate source activity. Subsequently, the MVARMA and ARIMA models are applied to the EEG sensor and active source data, enabling the calculation of model parameters. Principal component analysis is then utilized to select important parameters, and a K nearest neighbor classifier is employed to classify participants as either autistic or neurotypical. The results demonstrate superior classification performance, achieving higher accuracy compared to alternative methods. The proposed method yields superior classification outcomes when compared to other approaches, as it exhibits improved classification measures.