Bayesian Gaussian Process Classification from Event-Related Brain Potentials in Alzheimer’s Disease

  • Wolfgang Fruehwirt
  • Pengfei Zhang
  • Matthias Gerstgrasser
  • Dieter Grossegger
  • Reinhold Schmidt
  • Thomas Benke
  • Peter Dal-Bianco
  • Gerhard Ransmayr
  • Leonard Weydemann
  • Heinrich Garn
  • Markus Waser
  • Michael Osborne
  • Georg DorffnerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10259)


Event-related potentials (ERPs) have been shown to reflect neurodegenerative processes in Alzheimer’s disease (AD) and might qualify as non-invasive and cost-effective markers to facilitate the objectivization of AD assessment in daily clinical practice. Lately, the combination of multivariate pattern analysis (MVPA) and Gaussian process classification (GPC) has gained interest in the neuroscientific community. Here, we demonstrate how a MVPA-GPC approach can be applied to electrophysiological data. Furthermore, in order to account for the temporal information of ERPs, we develop a novel method that integrates interregional synchrony of ERP time signatures. By using real-life ERP recordings of a prospective AD cohort study (PRODEM), we empirically investigate the usefulness of the proposed framework to build neurophysiological markers for single subject classification tasks. GPC outperforms the probabilistic reference method in both tasks, with the highest AUC overall (0.802) being achieved using the new spatiotemporal method in the prediction of rapid cognitive decline.


Machine learning Gaussian process classification Event-related potentials Alzheimer’s disease Single subject classification 



The PRODEM study has been supported by the Austrian Research Promotion Agency FFG, project no. 827462, including financial contributions from Dr. Grossegger and Drbal GmbH, Vienna, Austria.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wolfgang Fruehwirt
    • 1
    • 2
  • Pengfei Zhang
    • 2
  • Matthias Gerstgrasser
    • 3
  • Dieter Grossegger
    • 4
  • Reinhold Schmidt
    • 5
  • Thomas Benke
    • 6
  • Peter Dal-Bianco
    • 7
  • Gerhard Ransmayr
    • 8
  • Leonard Weydemann
    • 1
  • Heinrich Garn
    • 9
  • Markus Waser
    • 9
  • Michael Osborne
    • 2
  • Georg Dorffner
    • 1
    Email author
  1. 1.Section for AI and Decision SupportMedical University of ViennaViennaAustria
  2. 2.Department of Engineering ScienceUniversity of OxfordOxfordUK
  3. 3.Department of Computer ScienceUniversity of OxfordOxfordUK
  4. 4.Dr. Grossegger & Drbal GmbHViennaAustria
  5. 5.Department of NeurologyMedical University of GrazGrazAustria
  6. 6.Department of NeurologyMedical University of InnsbruckInnsbruckAustria
  7. 7.Department of NeurologyMedical University of ViennaViennaAustria
  8. 8.Department of NeurologyLinz General HospitalLinzAustria
  9. 9.AIT Austrian Institute of Technology GmbHViennaAustria

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