# Electroencephalogram (EEG) for Delineating Objective Measure of Autism Spectrum Disorder (ASD) (Extended Version)

## TL;DR

This paper explores the use of EEG signals combined with machine learning algorithms to develop an objective, early diagnostic tool for Autism Spectrum Disorder, addressing current challenges in subjective diagnosis methods.

## Contribution

It presents a novel approach integrating EEG data with machine learning for ASD classification, aiming to improve early diagnosis accuracy and efficiency.

## Key findings

- EEG shows potential as a biomarker for ASD.
- Machine learning algorithms can classify ASD based on EEG features.
- Objective measures may enhance early diagnosis of ASD.

## Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize and communicate. Overall, ASD has a broad range of symptoms and severity; hence the term spectrum is used. One of the main contributors to ASD is known to be genetics. Up to date, no suitable cure for ASD has been found. Early diagnosis is crucial for the long-term treatment of ASD, but this is challenging due to the lack of a proper objective measures. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the electric signals of the brain via electrodes placed on various places on the scalp. These signals can be used to study complex neuropsychiatric issues. Studies have shown that EEG has the potential to be used as a biomarker for various neurological conditions including ASD. This chapter will outline the usage of EEG measurement for the classification of ASD using machine learning algorithms.