TY - GEN
T1 - Robust comparison of simultaneous EEG recordings using Kalman filters and Gaussian mixture models
AU - von Stein, Niels
AU - Schulte-Coerne, Jonas
AU - Jonas, Stephan M.
AU - Kutafina, Ekaterina
N1 - Publisher Copyright:
© 2019 The authors, AIT Austrian Institute of Technology and IOS Press.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Electroencephalography
KW - Latent class analysis
KW - Unsupervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85066461067&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-971-3-113
DO - 10.3233/978-1-61499-971-3-113
M3 - Conference contribution
C2 - 31118326
AN - SCOPUS:85066461067
T3 - Studies in Health Technology and Informatics
SP - 113
EP - 120
BT - dHealth 2019 - From eHealth to dHealth - Proceedings of the 13th Health Informatics Meets Digital Health Conference
A2 - Hayn, Dieter
A2 - Eggerth, Alphons
A2 - Schreier, Gunter
PB - IOS Press
T2 - 13th Health Informatics Meets Digital Health Conference: From eHealth to dHealth, dHealth 2019
Y2 - 28 May 2019 through 29 May 2019
ER -