  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.
