% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Saraiva:283107,
      author       = {Saraiva, João Areias and Dyrba, Martin and Becker, Martin
                      and Krause, Ludwig and Berger, Christoph and Kirste, Thomas
                      and Teipel, Stefan},
      title        = {{C}ross‐{S}ectional {A}ssociations {B}etween the
                      {E}lectroencephalogram and {C}ognitive {S}tatus: {T}oward
                      {S}calable {M}onitoring {S}olutions},
      journal      = {Alzheimer's and dementia},
      volume       = {21},
      number       = {S2},
      issn         = {1552-5260},
      reportid     = {DZNE-2026-00003},
      pages        = {e098538},
      year         = {2025},
      abstract     = {Background:Alzheimer's disease (AD) strains healthcare
                      systems in an aging population, emphasizing the need for
                      continuous cognitive decline monitoring and its early
                      detection. The Mini-Mental State Examination (MMSE) remains
                      a widely used and cost-effective diagnostic tool, with
                      efforts underway to adapt it for digital home-based
                      assessments, enabling more frequent monitoring while
                      minimizing patient burden and mobility. Similarly,
                      electroencephalograms (EEG) have been investigated to
                      monitor cognitive status in ambulatory settings. In this
                      cross-sectional study, we identified key EEG features
                      reflecting the cognitive decline process and assessed their
                      feasibility to estimate cognitive status using machine
                      learning (ML).Method:An international and diverse cohort
                      (France, Greece, Turkey, Argentina, Colombia) was gathered
                      comprising N = 510 older adults (40-98 years, $46\%$ male).
                      At the time of stationary EEG recording, subjects exhibited
                      MMSE scores ranging from 30 (cognitively normal) to 4
                      (severe dementia). A Gradient Boosting ML regressor was
                      developed to estimate their cognitive status based on their
                      EEG spectrum, complexity, and connectivity, focusing on
                      identifying features strongly associated with MMSE scores.
                      The model estimations were evaluated in a leave-one-out
                      cross-validation procedure.Result:Key EEG features
                      significantly correlated with MMSE scores included Hjorth
                      Complexity in the left temporal lobe (r=0.58), alpha
                      coherence between the left and right temporal lobes
                      (r=0.48), and beta occipital edge frequency (r=0.42). Eighty
                      combined EEG features were identified as predictors of
                      cognitive status. Using these features, the ML regressor
                      estimated cognitive status with an average error of 2.53
                      points in the MMSE scale $(95\%$ CI±5.36). The model
                      demonstrated strong predictive performance, achieving an R2
                      value of 0.80 between estimated and actual MMSE
                      scores.Conclusion:Specific EEG features, particularly those
                      of temporal and occipital activity, can serve as reliable
                      predictors of cognitive status. While cohort diversity
                      enhanced the generalizability of these findings, more EEG
                      recordings in the low MMSE range are needed to improve
                      regression performance. Longitudinal studies are required to
                      validate the tracking of intra-subject EEG activity changes
                      associated with cognitive decline. In the future, ML could
                      automate periodic monitoring assessments of cognitive health
                      based on EEG in its wearable and low-resolution format,
                      especially in regions with limited specialized staff and
                      imaging technology.},
      month         = {Jul},
      date          = {2025-07-27},
      organization  = {Alzheimer’s Association
                       International Conference, Toronto
                       (Canada), 27 Jul 2025 - 31 Jul 2025},
      cin          = {AG Teipel},
      ddc          = {610},
      cid          = {I:(DE-2719)1510100},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)1 / PUB:(DE-HGF)16},
      doi          = {10.1002/alz70856_098538},
      url          = {https://pub.dzne.de/record/283107},
}