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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: Aug 21, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

Do age-related differences in aperiodic neural activity explain differences in resting EEG alpha?

Ashley Merkin¹, Sabrina Sghirripa², Lynton Graetz¹

¹Lifespan Human Neurophysiology Group, School of Biomedicine, The University of Adelaide, Adelaide, Australia; Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute, Adelaide, Australia.

Neurobiology of Aging

|November 15, 2022

Summary

Aging slows brain activity, but aperiodic background noise in electroencephalography (EEG) might explain changes in alpha frequency and power. Accounting for this noise reveals age-related differences in alpha power diminish.

Keywords:

1/f Age Alpha Aperiodic Electroencephalography Oscillations

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Last Updated: Aug 21, 2025

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

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Area of Science:

Neuroscience
Gerontology
Signal Processing

Background:

Resting electroencephalography (EEG) reveals age-related declines in alpha-band oscillatory activity, characterized by slower frequencies and reduced amplitude.
The contribution of aperiodic neural activity to these age-related EEG changes remains under-explored.
Aperiodic activity, often modeled as 1/f-like background noise, significantly influences spectral analysis of brain activity.

Purpose of the Study:

To investigate if age-related differences in aperiodic EEG activity explain observed changes in peak alpha frequency and power.
To quantify age-related alterations in the aperiodic component of the resting EEG signal.
To determine the impact of correcting for aperiodic activity on age-group comparisons of alpha oscillations.

Main Methods:

Utilized the spectral parameterization (specparam) algorithm to model and assess aperiodic activity in EEG power spectra.
Analyzed resting EEG data from a large cohort of 85 younger and 92 older adults.
Compared spectral parameters, including aperiodic exponent and offset, between younger and older participants.

Main Results:

Older adults exhibited smaller aperiodic exponents and offsets compared to younger adults, indicating a flatter spectral slope and a broadband downward shift in power.
After correcting for aperiodic activity, peak alpha frequency remained significantly slower in older adults.
Statistical differences in peak alpha power between age groups were no longer significant after accounting for aperiodic activity.

Conclusions:

Aperiodic activity in resting EEG is demonstrably altered with advanced age.
The aperiodic component significantly influences the interpretation of age-related changes in alpha oscillations.
Future research on neural activity and aging should incorporate the characterization of aperiodic EEG signals.