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.

Electronic downloads

• http://www.src.org/library/publication/p068849/
• http://dx.doi.org/10.1109/ICASSP.2014.6853960

• 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.
Groups: tools