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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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Related Experiment Video

Updated: Aug 6, 2025

EEG Mu Rhythm in Typical and Atypical Development

EEG Mu Rhythm in Typical and Atypical Development

Published on: April 9, 2014

Decomposing age effects in EEG alpha power.

Marius Tröndle¹, Tzvetan Popov², Andreas Pedroni²

¹Department of Psychology, University of Zurich, Methods of Plasticity Research, Zurich, Switzerland; University Research Priority Program (URPP) Dynamics of Healthy Aging, Zurich, Switzerland; Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.

Cortex; a Journal Devoted to the Study of the Nervous System and Behavior

|March 18, 2023

Summary

This summary is machine-generated.

Healthy aging research shows alpha power decreases with age. New analysis reveals this age effect is overestimated due to non-oscillatory brain signals, highlighting the need for advanced EEG analysis.

Keywords:

Aging Alpha power Aperiodic signal EEG

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Last Updated: Aug 6, 2025

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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

Area of Science:

Neuroscience
Gerontology
Signal Processing

Background:

Increasing life expectancy necessitates understanding brain changes in healthy aging.
Previous electroencephalography (EEG) studies reported age-related decreases in alpha oscillation power.
Non-oscillatory (aperiodic) signal components may confound these findings, requiring re-evaluation.

Purpose of the Study:

To re-investigate age-related changes in brain activity using a novel EEG decomposition method.
To determine if age-related alpha power differences persist after accounting for aperiodic components.
To establish reliable markers for the aging brain.

Main Methods:

Analysis of resting-state EEG data from 533 healthy young and elderly individuals across three independent samples.
Utilized a new algorithm to decompose EEG signals into periodic and aperiodic components.
Applied multivariate sequential Bayesian updating to analyze age effects in each signal component.

Main Results:

Replicated the age-related decrease in total alpha power.
Observed age-related decreases in the intercept and slope of the aperiodic signal component.
Found that accounting for the aperiodic component reduces the apparent age effect on alpha power, suggesting overestimation in previous analyses.

Conclusions:

Separating periodic and aperiodic signal components is crucial for accurate analysis of neural power spectra.
Despite accounting for confounds, robust evidence shows aging is associated with decreased aperiodic-adjusted alpha power.
Newly developed measures are reliable markers of the aging brain, warranting further investigation into their relation with cognitive decline.