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Related Experiment Video

Updated: Jun 13, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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An Effective LRSF-DLNN-Based Autism Spectrum Disorder Prediction Using EEG and fMRI.

Sahunthala S1, Malathi S2, TamilThendral M3

  • 1Department of Information Technology, Easwari Engineering College, Anna University, Chennai, India.

Developmental Neurobiology
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning model for early autism spectrum disorder (ASD) prediction using electroencephalogram (EEG) and functional MRI (fMRI) data. The proposed method achieves high accuracy, improving diagnostic efficiency for ASD.

Keywords:
autism spectrum disorder (ASD)distance functional green anaconda optimization (DFGAO)electroencephalogram (EEG)functional magnetic resonance imaging (fMRI)logistic regression with scaled function‐based deep learning neural network (LRSF‐DLNN)ocular motilityweighted penalty factor‐based variational mode decomposition (WPFVMD)

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Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
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Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Related Experiment Videos

Last Updated: Jun 13, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)
10:02

Event Related Potentials (ERPs) and other EEG Based Methods for Extracting Biomarkers of Brain Dysfunction: Examples from Pediatric Attention Deficit/Hyperactivity Disorder (ADHD)

Published on: March 12, 2020

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Autism Spectrum Disorder (ASD) is a prevalent neurological condition impacting communication and behavior.
  • Early ASD prediction is crucial for enhancing patient outcomes.
  • Existing diagnostic methods using EEG and MRI show limitations due to noise and misclassification.

Purpose of the Study:

  • To propose an efficient ASD prediction model using EEG and fMRI data.
  • To overcome the limitations of existing diagnostic techniques.
  • To improve the accuracy and reliability of early ASD detection.

Main Methods:

  • A novel logistic regression with scaled function-based deep learning neural network (LRSF-DLNN) model was developed.
  • Preprocessing involved noise removal for EEG (cosine-based Butterworth filter) and fMRI (slice time correction, GF filter).
  • EEG data was decomposed using weighted penalty factor-centric variational mode decomposition (WPFVMD) for alpha and theta band estimation via SSTD-ERSP. Feature extraction, selection (DFGAO), and classification were performed.

Main Results:

  • The proposed LRSF-DLNN model demonstrated superior performance in ASD prediction.
  • The technique achieved a high accuracy rate of 98.8%.
  • The model outperformed existing prevailing methods in diagnostic accuracy.

Conclusions:

  • The developed LRSF-DLNN model offers a highly accurate and efficient approach for early ASD prediction.
  • The integration of advanced signal processing and deep learning techniques enhances diagnostic capabilities.
  • This method holds significant potential for improving the management and lifecycle of individuals with ASD.