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Related Experiment Video

Updated: Sep 28, 2025

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

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

EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals.

Dahiru Tanko¹, Prabal Datta Barua^2,3,4, Sengul Dogan¹

¹Department of Digital Forensics Engineering, Technology Faculty, Firat University, Elazig, Turkey.

Physiological Measurement

|April 4, 2022

Summary

A novel handcrafted feature extraction method, EPSPatNet86, effectively detects Attention-Deficit Hyperactivity Disorder (ADHD) using electroencephalography (EEG) signals. The model achieved high accuracy, demonstrating its potential for diagnosing brain abnormalities from EEG data.

Keywords:

ADHD EEG signal classification EPSPatNet86 feature engineering hand-modeled learning network

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Cortical Source Analysis of High-Density EEG Recordings in Children

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Last Updated: Sep 28, 2025

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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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

Area of Science:

Biomedical Engineering
Signal Processing
Computational Neuroscience

Background:

Attention-Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder.
Electroencephalography (EEG) signals offer a non-invasive window into brain activity.
Accurate classification of ADHD from EEG is crucial for timely diagnosis and intervention.

Purpose of the Study:

To introduce a novel handcrafted feature extraction system, EPSPatNet86, for ADHD detection using EEG signals.
To develop a robust machine learning model capable of identifying EEG signal abnormalities indicative of ADHD.
To evaluate the classification performance of the proposed system on a dedicated ADHD EEG dataset.

Main Methods:

A handcrafted feature extraction method utilizing a directed graph and an eight-pointed star pattern (EPSPat) was developed.
The system incorporated tunable q wavelet transforms (TQWT) and wavelet packet decomposition (WPD) to extract 85 wavelet coefficient bands.
Feature selection was performed using an iterative Chi2 (IChi2) selector, and classification was executed with a k-nearest neighbors (kNN) classifier, forming the EPSPatNet86 model.

Main Results:

The EPSPatNet86 model demonstrated high accuracy in detecting ADHD EEG signals.
Achieved 97.19% accuracy with 10-fold cross-validation.
Attained 87.60% accuracy using subject-wise validation.

Conclusions:

The developed EPSPatNet86 model exhibits significant potential for classifying EEG signals and detecting abnormalities.
The system's performance indicates its suitability for ADHD diagnosis.
The methodology can be extended to analyze other EEG datasets for various neurological conditions.