Advanced qEEG analyses discriminate between dementia subtypes

Masha Dolores Burelo Segura, Jack Bray, Olga Gulka, Michael J Firbank, John-Paul Taylor, Bettina Platt* (Corresponding Author)

*Corresponding author for this work

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Dementia is caused by neurodegenerative conditions and characterized by cognitive decline. Diagnostic accuracy for dementia subtypes, such as Alzheimer’s Disease (AD), Dementia with Lewy Bodies (DLB) and Parkinson’s Disease with dementia (PDD), remains challenging.

Here, different methods of quantitative electroencephalography (qEEG) analyses were employed to assess their effectiveness in distinguishing dementia subtypes from healthy controls under eyes closed (EC) and eyes open (EO) conditions.

Classic Fast-Fourier Transform (FFT) and autoregressive (AR) power analyses differentiated between all conditions for the 4-8 Hz theta range. Only individuals with dementia with Lewy Bodies (DLB) differed from healthy subjects for the wider 4-15 Hz frequency range, encompassing the actual dominant frequency of all individuals. 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, also discriminating between synucleinopathies vs controls and AD.

Dissociation of periodic oscillations and aperiodic components of AR spectra using Fitting-Oscillations-&-One-Over-F (FOOOF) modelling delivered a reliable and subtype-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 from age and cognitive status.

Our findings indicate that qEEG methods can reliably detect dementia subtypes. Spectral analyses comprising an integrated, multi-parameter EEG approach discriminating between periodic and aperiodic components under EC and EO conditions may enhance diagnostic accuracy in the future.
Original languageEnglish
Article number110195
JournalJournal of Neuroscience Methods
Early online date16 Jun 2024
Publication statusE-pub ahead of print - 16 Jun 2024

Bibliographical note

This study is part of the academic postgraduate research of Masha Burelo, financed through the postgraduate fellowship program of the National Council of Science and Technology (CONACYT, by its Spanish acronym) of Mexico.

Data Availability Statement

Data will be made available on request.


  • Electroencephalography
  • Dementia
  • Lewy Body
  • Parkinson
  • Alzheimer
  • Spectral Analysis


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