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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Related Experiment Video

Updated: May 6, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Brain-region specific autism prediction from electroencephalogram signals using graph convolution neural network.

Neha Prerna Tigga¹, Shruti Garg¹, Nishant Goyal²

¹Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, India.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|June 29, 2024

Summary

Graph Convolutional Neural Networks (GCNNs) show promise in autism spectrum disorder (ASD) detection using EEG data. The anterior-frontal region of the brain was most predictive, achieving 87.07% accuracy in identifying ASD.

Keywords:

Autism brain region deep learning electroencephalogram graph convolution neural network

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Last Updated: May 6, 2026

Cortical Source Analysis of High-Density EEG Recordings in Children

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Neuroscience
Computational Neuroscience
Biomedical Engineering

Background:

Brain variations contribute to developmental disorders like autism spectrum disorder (ASD).
Electroencephalography (EEG) signals offer valuable insights into brain function abnormalities for detecting neurological conditions.

Purpose of the Study:

To investigate the efficacy of a Graph Convolutional Neural Network (GCNN) for predicting ASD.
To analyze EEG data from autistic and typically developing children to identify neurological markers of ASD.

Main Methods:

EEG data were collected from 8 autistic and 8 typically developing children.
A GCNN model was employed for ASD prediction following autoregressive and spectral feature extraction.
EEG data utilized 257 channels, with 71 channels (10-10 international equivalents) analyzed across 12 brain regions.

Main Results:

The anterior-frontal brain region demonstrated the highest predictive capability for ASD.
The GCNN model achieved an accuracy of 87.07% in ASD prediction.
This highlights the GCNN method's suitability for EEG-based ASD detection.

Conclusions:

The study provides a detailed dataset that deepens the understanding of the neurological underpinnings of ASD.
Findings can assist healthcare practitioners in the diagnosis of ASD.
The GCNN approach shows potential for improving ASD detection through EEG analysis.