Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data
Finke A, Lange M, Steppacher I, Kißler J, Ritter H (2019)
Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin.
Kurzbeitrag Konferenz / Poster | Englisch
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Autor*in
herausgebende Körperschaft
IEEE EMBS
Einrichtung
Abstract / Bemerkung
We propose to use data-driven, unsupervised clustering techniques based on Autoencoder Networks (AEN) combined with Gaussian Mixture Models (GMM) to cluster EEG recordings from patients with Disorders of Consciousness (DoC). We evaluate the results by investigating the structures obtained after clustering and compare them between individual patients. We observe a good stability of cluster assignments throughout time. For some patients clusters arise which specif- ically cover the onsets of auditory stimulation.
Erscheinungsjahr
2019
Konferenz
41th International Engineering in Medicine and Biology Conference (EMBC '19)
Konferenzort
Berlin
Konferenzdatum
2019-07-23 – 2019-07-27
Page URI
https://pub.uni-bielefeld.de/record/2941701
Zitieren
Finke A, Lange M, Steppacher I, Kißler J, Ritter H. Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data. Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin.
Finke, A., Lange, M., Steppacher, I., Kißler, J., & Ritter, H. (2019). Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data. Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin.
Finke, Andrea, Lange, Martin, Steppacher, Inga, Kißler, Johanna, and Ritter, Helge. 2019. “Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data”. Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin , ed. IEEE EMBS.
Finke, A., Lange, M., Steppacher, I., Kißler, J., and Ritter, H. (2019).“Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data”. Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin.
Finke, A., et al., 2019. Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data. Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin.
A. Finke, et al., “Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data”, Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin, 2019.
Finke, A., Lange, M., Steppacher, I., Kißler, J., Ritter, H.: Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data. Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin (2019).
Finke, Andrea, Lange, Martin, Steppacher, Inga, Kißler, Johanna, and Ritter, Helge. “Unsupervised Clustering of Wakefulness Levels with Autoencoder Networks and Gaussian Mixture Models on EEG Single-Trial Data”. Presented at the 41th International Engineering in Medicine and Biology Conference (EMBC '19), Berlin, 2019.
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