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Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....

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Updated: Sep 3, 2025

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

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Attention Deficit Hyperactivity Disorder Assessment Based on Patient Behavior Exhibited in a Car Video Game: A Pilot

Aaron Sujar^1,2, Sofia Bayona^1,3, David Delgado-Gómez⁴

¹Department of Computer Engineering, Universidad Rey Juan Carlos, 28933 Madrid, Spain.

|July 27, 2022

Summary

This study introduces a novel system for observing Attention Deficit Hyperactivity Disorder (ADHD) behaviors in children using a video game. Findings reveal age-related performance differences and potential for classifying ADHD symptom patterns.

Keywords:

ADHD attention deficits attention span behavioral patterns e-health hyperactivity inattention neurodevelopmental disorders video games

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Area of Science:

Neuroscience
Developmental Psychology
Human-Computer Interaction

Background:

Attention Deficit Hyperactivity Disorder (ADHD) diagnosis relies on subjective observation, impacting early intervention.
Objective behavioral measures are needed to supplement clinical ADHD assessments.
Video games offer a controlled environment for detailed behavioral analysis.

Purpose of the Study:

To introduce an information recording system for detailed behavioral analysis of children with ADHD during a video game task.
To investigate behavioral patterns and performance metrics in diagnosed ADHD children.
To explore the relationship between age, performance, and behavioral patterns in ADHD.

Main Methods:

Developed a system to continuously record participant activity and calculate errors during a car driving video game.
Analyzed trajectory graphs to identify distinct behavioral patterns (uniform, attention loss, attention/inattention intervals).
Applied clustering analysis to age-over-time position data for performance-based classification.

Main Results:

A significant dependence was observed between children's age and their performance in the video game task.
Behavioral patterns varied among participants, including consistent performance, attention lapses, and fluctuating attention.
Clustering analysis successfully classified children based on their performance patterns over time.

Conclusions:

The proposed system provides detailed insights into ADHD-related behavioral patterns during a gamified task.
Age is a significant factor influencing performance in children with ADHD.
This methodology shows promise for objective, performance-based classification of ADHD symptom presentation.