EEG-Based Anamoly Detection for Autistic Kids – A Pilot Study

Abstract

An electroencephalogram (EEG) test can be utilized to capture the electrical impulses in the human brain. EEG signal analysis is crucial in the detection and treatment of brain diseases. Autism is one of the neurological disorders that needs to be diagnosed in the early stages of life. Autistic behavior is difficult to differentiate and it can even lead to adverse effects in the daily routine of a kid. Recent advances in Artificial Intelligence have proven to be an effective way of diagnosing ASD. This research employs PyCaret framework to analyze the anomalies present in the EEG signal data in the context of differentiating Autistic children from Typically developing children. The different anomaly detection modules have been used to detect anomalies, compute their anomaly scores and visualize it. The goal of this study is to determine if PyCaret's anomaly detection module can aid the detection of ASD.