Background: Dementia is caused by neurodegenerative conditions and characterizedby cognitive decline. Diagnostic accuracy for dementia subtypes, such as Alzheimer’sDisease (AD), Dementia with Lewy Bodies (DLB) and Parkinson’s Disease withdementia (PDD), remains challenging.Methods: Here, different methods of quantitative electroencephalography (qEEG)analyses were employed to assess their effectiveness in distinguishing dementiasubtypes from healthy controls under eyes closed (EC) and eyes open (EO)conditions.Results: Classic Fast-Fourier Transform (FFT) and autoregressive (AR) poweranalyses differentiated between all conditions for the 4-8 Hz theta range. Onlyindividuals with dementia with Lewy Bodies (DLB) differed from healthy subjects for thewider 4-15 Hz frequency range, encompassing the actual dominant frequency of allindividuals. FFT results for this range yielded wider significant discriminators vs AR,also detecting differences between AD and DLB. Analyses of the inclusive dominant /peak frequency range (4-15 Hz) indicated slowing and reduced variability, alsodiscriminating between synucleinopathies vs controls and AD.Dissociation of periodic oscillations and aperiodic components of AR spectra usingFitting-Oscillations-&-One-Over-F (FOOOF) modelling delivered a reliable andsubtype-specific slowing of brain oscillatory peaks during EC and EO for all groups.Distinct and robust differences were particularly strong for aperiodic parameters,suggesting their potential diagnostic power in detecting specific changes resulting fromage and cognitive status.Conclusion: Our findings indicate that qEEG methods can reliably detect dementiasubtypes. Spectral analyses comprising an integrated, multi-parameter EEG approachdiscriminating between periodic and aperiodic components under EC and EOconditions may enhance diagnostic accuracy in the future
