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 languageEnglish (US)
Title of host publicationConference Record - Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages607-611
Number of pages5
Volume2016-February
ISBN (Print)9781467385763
DOIs
StatePublished - Feb 26 2016
Event49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States
Duration: Nov 8 2015Nov 11 2015

Other

Other49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
CountryUnited States
CityPacific Grove
Period11/8/1511/11/15

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

Cite this

Solis, F., Maurer, A., Jiang, J., & Papandreou-Suppappola, A. (2016). Adaptive EEG artifact suppression using Gaussian mixture modeling. In Conference Record - Asilomar Conference on Signals, Systems and Computers (Vol. 2016-February, pp. 607-611). [7421202] IEEE Computer Society. https://doi.org/10.1109/ACSSC.2015.7421202