Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures a...

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Published inBioengineering (Basel) Vol. 11; no. 5; p. 418
Main Authors Kang, Jae-Hwan, Bae, Jang-Han, Jeon, Young-Ju
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 01.05.2024
Subjects
Age
Age factors in disease
Age groups
age-related AI models
age-related changes
Aging
Analysis
analytic methods
Artificial intelligence
Brain
Cognition
Deep learning
EEG
Electroencephalography
Health aspects
Machine learning
Magnetic resonance imaging
Methods
Morphology
Neurophysiology
Neurosciences
resting-state EEG signals
Signal processing
Temporal resolution
South Korea
age-related AI models
analytic methods
age-related changes
resting-state EEG signals
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Summary:The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.
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ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering11050418