Research Article Open Access

Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network

Zahrul Jannat Peya1, M. A. H. Akhand1, Jannatul Ferdous Srabonee1 and Nazmul Siddique2
  • 1 Department of Computer Science and Engineering, Khulna University of Engineering &Technology, Bangladesh
  • 2 School of Computing, Engineering and Intelligent Systems, Ulster University, United Kingdom


Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder relating to speech complications, nonverbal and social communication, and repetitive behaviors. There is no remedy for ASD but early diagnosis, mediation, and supportive care can aid the development of language, conduct, and communication skills. As the cause of ASD is a neurodevelopmental disorder, its diagnosis based on brain function analyzing different brain signals, especially Electroencephalography (EEG), has drawn attention recently. Brain activity is recorded over time as an EEG signal from the scalp of a human and is used to investigate complicated neuropsychiatric disorders in the brain. In this study, the data from the EEG channels are translated into two-Dimensional (2D) form through correlation, and classification is performed using Convolutional Neural Networks (CNN), the well-known deep learning method for image analysis and classification. Two different CNN models are considered for classification purposes: Generic CNN and Residual Network (ResNet), a well-known deep CNN model. The proposed method with Resnet achieved 88% classification accuracy on a five-fold cross-validation mode, whereas it was 100 on 20% of test samples. Experimental evaluations using clinical EEG data revealed the efficacy of the proposed method outperforming other existing methods.

Journal of Computer Science
Volume 18 No. 8, 2022, 695-704


Submitted On: 20 April 2022 Published On: 31 July 2022

How to Cite: Peya, Z. J., Akhand, M. A. H., Srabonee, J. F. & Siddique, N. (2022). Autism Detection from 2D Transformed EEG Signal using Convolutional Neural Network. Journal of Computer Science, 18(8), 695-704.

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  • Autism Spectrum Disorder
  • Convolutional Neural Network
  • Electroencephalography
  • Pearson’s Correlation Coefficient
  • Residual Neural Network