The aging process is accompanied by changes in brain wave patterns identified also through
EEG signal alterations. Recently, with the increasing burden of dementia and dementia-related cognitive decline, more research has been conducted to understand how brain waves
change with pathological aging and whether there are biomarkers that could help with early
detection of dementia or cognitive decline. The EEG biomarker, peak alpha frequency (PAF),
is shown to be a robust correlate of cognitive abilities. However, literature also shows
potential influences of other factors such as age, gender, years of education, and head size.
The effects of these factors have been studied in the past but to a limited extent. In the current
study, we included a large sample of older individuals (N = 448) with different cognitive
abilities from different parts of Slovenia, with the aim of providing a more representative
sample. We examined which of the investigated variables (cognitive status, age, sex, years of
education, head size) best predicts PAF. We captured 8-minute resting-state EEG data, and
determined PAF values using an automated FOOOF algorithm on segments with closed
eyes. Cognitive status was assessed based on the results of four widely used psychometric
tests for evaluating cognitive abilities in older adults: MoCA, ADAS-cog, Eurotest, and
Phototest. Our results highlight that despite the significant influence of age on PAF, cognitive
status emerges as the most robust predictor of PAF. Factors sex, years of education, and head
size did not prove to be crucial in determining PAF. Therefore, the PAF biomarker, when
considered age alongside, demonstrates its utility in providing a broad assessment of
cognitive status in older adults. We emphasize the practical value of PAF, particularly in
combination with other (EEG) biomarkers, for the early detection of dementia
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