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Brain Waves

Brain Waves

Brain waves are electrical signals generated by the neurons in the brain, which are regularly monitored to measure mental activities. Brain waves and their frequency ranges can be measured using an electroencephalogram or EEG. There are four main types of brain waves, each with distinct characteristics:

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Updated: Oct 29, 2025

Combining Computer Game-Based Behavioural Experiments With High-Density EEG and Infrared Gaze Tracking

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Published on: December 16, 2010

Computer Gaming and Physiological Changes in the Brain: An Insight from QEEG Complexity Analysis.

Zahrasadat Hosseini¹, Roya Delpazirian¹, Hossein Lanjanian²

¹Institute for Cognitive Science Studies, Tehran, Iran.

Applied Psychophysiology and Biofeedback

|July 13, 2021

Summary

Brain wave complexity, not power, distinguishes video game addicts from healthy individuals. The Higuchi algorithm accurately identifies gaming disorder using EEG complexity.

Keywords:

Adolescent Brain wave complexity Features selection Higuchi fractal dimension Machine learning Video gaming addiction

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Area of Science:

Neuroscience
Computational Psychiatry

Background:

Video gaming disorder is a growing concern.
Quantitative EEG (QEEG) analysis may reveal neural differences in addicts.
Previous studies often focused on EEG power, not signal complexity.

Purpose of the Study:

To compare brain wave patterns between video game addicts and healthy controls.
To investigate the utility of signal complexity measures in identifying gaming disorder.
To assess the effectiveness of the Higuchi algorithm and multilayer perceptron for classification.

Main Methods:

A case-control study involving 30 male video game addicts (14-20 years) and 20 healthy controls.
QEEG data collected during closed-eye, open-eye states, and a working memory task.
Higuchi algorithm used for feature extraction of EEG signal complexity.
Multilayer perceptron classifier employed for distinguishing between groups.

Main Results:

No significant differences in EEG power ratios were found between groups.
The Higuchi algorithm achieved over 95% precision in classifying addicts and controls.
Significant differences in Higuchi Fractal Dimension were observed in specific EEG channels.

Conclusions:

Brain wave complexity, rather than power, is a key differentiator in QEEG analysis for gaming disorder.
The Higuchi algorithm is effective for feature extraction in classifying brain waves related to gaming disorder.
EEG complexity analysis holds promise for understanding and diagnosing gaming disorder.