Document Type
Article
Publication Date
2019
DOI
10.3844/jcssp.2019.1161.1183
Publication Title
Journal of Computer Science
Volume
15
Issue
8
Pages
1161-1183
Abstract
Autism Spectrum Disorder is a lifelong neurodevelopmental condition which affects social interaction, communication and behaviour of an individual. The symptoms are diverse with different levels of severity. Recent studies have revealed that early intervention is highly effective for improving the condition. However, current ASD diagnostic criteria are subjective which makes early diagnosis challenging, due to the unavailability of well-defined medical tests to diagnose ASD. Over the years, several objective measures utilizing abnormalities found in EEG signals and statistical analysis have been proposed. Machine learning based approaches provide more flexibility and have produced better results in ASD classification. This paper presents a survey of major EEG-based ASD classification approaches from 2010 to 2018, which adopt machine learning. The methodology is divided into four phases: EEG data collection, pre-processing, feature extraction and classification. This study explores different techniques and tools used for pre-processing, feature extraction and feature selection techniques, classification models and measures for evaluating the model. We analyze the strengths and weaknesses of the techniques and tools. Further, this study summarizes the ASD classification approaches and discusses the existing challenges, limitations and future directions.
Original Publication Citation
Brihadiswaran, G., Haputhanthri, D., Gunathilaka, S., Meedeniya, D., & Jayarathna, S. (2019). EEG-based processing and classification methodologies for autism spectrum disorder: A review. Journal of Computer Science, 15(8), 1161-1183. doi:10.3844/jcssp.2019.1161.1183
Repository Citation
Brihadiswaran, G., Haputhanthri, D., Gunathilaka, S., Meedeniya, D., & Jayarathna, S. (2019). EEG-based processing and classification methodologies for autism spectrum disorder: A review. Journal of Computer Science, 15(8), 1161-1183. doi:10.3844/jcssp.2019.1161.1183
ORCID
0000-0002-4879-7309 (Jayarathna)
Comments
© 2019 Gunavaran Brihadiswaran, Dilantha Haputhanthri, Sahan Gunathilaka, Dulani Meedeniya and Sampath Jayarathna. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.