Automatic Removal of EEG Artifacts using Electrode-Scalp Impedance
Yuan Zou, Omid Dehzangi, Roozbeh Jafari

Citation
Yuan Zou, Omid Dehzangi, Roozbeh Jafari. "Automatic Removal of EEG Artifacts using Electrode-Scalp Impedance". International Conference on Acoustics, Speech and Signal Processing, 4, May, 2014.

Abstract
Due to the low signal-to-noise ratio of electroencephalographic (EEG) recordings, the quality of the electrode-scalp contact is an important factor in EEG-based brain-computer interfaces (BCIs). For this reason, the impedance between each individual electrode and the scalp is measured prior to each EEG recording session. In order to obtain high quality EEG signals and accurate performance, the impedance has to be low (below 5K Ohms). Typically, researchers have reduced the electrode-scalp impedance by performing time-consuming electrode adjustments prior to the data acquisition stage. In this paper, we utilize the electrode-scalp impedance information to remove the EEG artifacts caused by high impedance electrodes in order to enhance the signal quality during the signal processing stage. Our proposed method is based on the independent component analysis (ICA) algorithm, which is used to decompose the EEG signals into independent components. The electrode-scalp impedance is employed to automatically distinguish irrelevant components from event-related components. The experimental results show that our method can effectively remove artifacts and enhance the BCI performance compared to the scenario where no artifacts were removed, and the scenario in which irrelevant independent components were removed manually based on prior knowledge.

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Citation formats  
  • HTML
    Yuan Zou, Omid Dehzangi, Roozbeh Jafari. <a
    href="http://www.terraswarm.org/pubs/232.html"
    >Automatic Removal of EEG Artifacts using Electrode-Scalp
    Impedance</a>, International Conference on Acoustics,
    Speech and Signal Processing, 4, May, 2014.
  • Plain text
    Yuan Zou, Omid Dehzangi, Roozbeh Jafari. "Automatic
    Removal of EEG Artifacts using Electrode-Scalp
    Impedance". International Conference on Acoustics,
    Speech and Signal Processing, 4, May, 2014.
  • BibTeX
    @inproceedings{ZouDehzangiJafari14_AutomaticRemovalOfEEGArtifactsUsingElectrodeScalpImpedance,
        author = {Yuan Zou and Omid Dehzangi and Roozbeh Jafari},
        title = {Automatic Removal of EEG Artifacts using
                  Electrode-Scalp Impedance},
        booktitle = {International Conference on Acoustics, Speech and
                  Signal Processing},
        day = {4},
        month = {May},
        year = {2014},
        abstract = {Due to the low signal-to-noise ratio of
                  electroencephalographic (EEG) recordings, the
                  quality of the electrode-scalp contact is an
                  important factor in EEG-based brain-computer
                  interfaces (BCIs). For this reason, the impedance
                  between each individual electrode and the scalp is
                  measured prior to each EEG recording session. In
                  order to obtain high quality EEG signals and
                  accurate performance, the impedance has to be low
                  (below 5K Ohms). Typically, researchers have
                  reduced the electrode-scalp impedance by
                  performing time-consuming electrode adjustments
                  prior to the data acquisition stage. In this
                  paper, we utilize the electrode-scalp impedance
                  information to remove the EEG artifacts caused by
                  high impedance electrodes in order to enhance the
                  signal quality during the signal processing stage.
                  Our proposed method is based on the independent
                  component analysis (ICA) algorithm, which is used
                  to decompose the EEG signals into independent
                  components. The electrode-scalp impedance is
                  employed to automatically distinguish irrelevant
                  components from event-related components. The
                  experimental results show that our method can
                  effectively remove artifacts and enhance the BCI
                  performance compared to the scenario where no
                  artifacts were removed, and the scenario in which
                  irrelevant independent components were removed
                  manually based on prior knowledge.},
        URL = {http://terraswarm.org/pubs/232.html}
    }
    

Posted by Barb Hoversten on 11 Dec 2013.
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