Robust comparison of simultaneous EEG recordings using Kalman filters and Gaussian mixture models

Niels von Stein, Jonas Schulte-Coerne, Stephan M. Jonas, Ekaterina Kutafina

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this manuscript we propose a novel method to compare simultaneously recorded electroencephalography (EEG) signals from different devices. Although standard methods like correlation and spectral analysis give quantitative answers to this question, these methods often penalize certain artifacts such as eye blinking too strongly. In our analysis we instead utilize an unsupervised labeling technique to evaluate the matching of two signals by comparing their label sequences. The proposed method was successfully tested on artificial data, where it showed a reduced deviation from the ground truth compared to the correlation coefficient. Furthermore, the method was applied on a real use-case to assess the quality of a low-cost EEG device compared to a clinical one. Here it showed more consistent results than the correlation coefficient, while it also did not rely on outlier removal prior to the analysis. However, the proposed method still suffers from accidental matches of labels, so that unrelated data sets may be assigned an unexpectedly high matching score. This paper suggests extensions to the proposed method, which could improve this issue.

Original languageEnglish
Title of host publicationdHealth 2019 - From eHealth to dHealth - Proceedings of the 13th Health Informatics Meets Digital Health Conference
EditorsDieter Hayn, Alphons Eggerth, Gunter Schreier
PublisherIOS Press
Pages113-120
Number of pages8
ISBN (Electronic)9781614999706
DOIs
StatePublished - 2019
Event13th Health Informatics Meets Digital Health Conference: From eHealth to dHealth, dHealth 2019 - Vienna, Austria
Duration: 28 May 201929 May 2019

Publication series

NameStudies in Health Technology and Informatics
Volume260
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference13th Health Informatics Meets Digital Health Conference: From eHealth to dHealth, dHealth 2019
Country/TerritoryAustria
CityVienna
Period28/05/1929/05/19

Keywords

  • Electroencephalography
  • Latent class analysis
  • Unsupervised machine learning

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