Adaptive EEG artifact suppression using Gaussian mixture modeling

Francisco Solis; A. Maurer; J. Jiang; Antonia Papandreou-Suppappola

doi:10.1109/ACSSC.2015.7421202

Adaptive EEG artifact suppression using Gaussian mixture modeling

Francisco Solis, A. Maurer, J. Jiang, Antonia Papandreou-Suppappola

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

Neural tracking using electroencephalography (EEG) recordings suffers from physiologic and extraphysiologic artifacts. We propose an integrated method to adaptively track multiple neural sources while reducing the effects of artifacts. Time-frequency features are first extracted from EEG recordings without pre-processing to suppress artifacts. Unsupervised clustering using Gaussian mixture modeling is then used to separate sources from artifacts, and the clustering results are incorporated into a probability hypothesis density filter to estimate the parameters of an unknown number of sources. Simulation results demonstrate the method's effectiveness in increasing the tracking accuracy performance for multiple neural sources using recordings contaminated by artifacts.

Original language	English (US)
Title of host publication	Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Editors	Michael B. Matthews
Publisher	IEEE Computer Society
Pages	607-611
Number of pages	5
ISBN (Electronic)	9781467385763
DOIs	https://doi.org/10.1109/ACSSC.2015.7421202
State	Published - Feb 26 2016
Event	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
Volume	2016-February
ISSN (Print)	1058-6393

Other

Other	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	11/8/15 → 11/11/15

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2015.7421202

Cite this

Solis, F., Maurer, A., Jiang, J., & Papandreou-Suppappola, A. (2016). Adaptive EEG artifact suppression using Gaussian mixture modeling. In M. B. Matthews (Ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 (pp. 607-611). Article 7421202 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421202

Adaptive EEG artifact suppression using Gaussian mixture modeling. / Solis, Francisco; Maurer, A.; Jiang, J. et al.
Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. ed. / Michael B. Matthews. IEEE Computer Society, 2016. p. 607-611 7421202 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2016-February).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Solis, F, Maurer, A, Jiang, J & Papandreou-Suppappola, A 2016, Adaptive EEG artifact suppression using Gaussian mixture modeling. in MB Matthews (ed.), Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015., 7421202, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2016-February, IEEE Computer Society, pp. 607-611, 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 11/8/15. https://doi.org/10.1109/ACSSC.2015.7421202

Solis F, Maurer A, Jiang J, Papandreou-Suppappola A. Adaptive EEG artifact suppression using Gaussian mixture modeling. In Matthews MB, editor, Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015. IEEE Computer Society. 2016. p. 607-611. 7421202. (Conference Record - Asilomar Conference on Signals, Systems and Computers). doi: 10.1109/ACSSC.2015.7421202

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AB - Neural tracking using electroencephalography (EEG) recordings suffers from physiologic and extraphysiologic artifacts. We propose an integrated method to adaptively track multiple neural sources while reducing the effects of artifacts. Time-frequency features are first extracted from EEG recordings without pre-processing to suppress artifacts. Unsupervised clustering using Gaussian mixture modeling is then used to separate sources from artifacts, and the clustering results are incorporated into a probability hypothesis density filter to estimate the parameters of an unknown number of sources. Simulation results demonstrate the method's effectiveness in increasing the tracking accuracy performance for multiple neural sources using recordings contaminated by artifacts.

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Adaptive EEG artifact suppression using Gaussian mixture modeling

Abstract

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