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Related Experiment Video

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Deep Brain Stimulation with Simultaneous fMRI in Rodents
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Removing deep brain stimulation artifacts from the electroencephalogram: Issues, recommendations and an open-source

Guillaume Lio1, Stéphane Thobois2, Bénédicte Ballanger3

  • 1Université de Lyon, F-69622 Lyon, France; Université Lyon 1, Villeurbanne, France; CNRS, Centre de Neuroscience Cognitive, Bron, France.

Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology
|August 26, 2018
PubMed
Summary

Deep brain stimulation (DBS) creates electrical artifacts in EEG recordings, obscuring brain activity. This review details artifact removal methods and highlights an open-source toolbox for improved analysis in DBS patients.

Keywords:
AntialiasingArtifactsDeep brain stimulationEEGHampelICALow-pass filteringMEGMatched filtersOversamplingTemplate subtraction

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Deep brain stimulation (DBS) is crucial for treating neurological disorders.
  • Understanding DBS effects requires analyzing brain activity with electroencephalography (EEG).
  • DBS generates significant electrical artifacts that contaminate EEG signals, hindering neural activity assessment.

Purpose of the Study:

  • To systematically review and discuss methods for removing DBS-induced artifacts from EEG data.
  • To identify challenges and limitations associated with current artifact removal techniques.
  • To introduce a user-friendly, open-source solution for DBS artifact mitigation.

Main Methods:

  • Review of signal processing techniques for artifact removal.
  • Categorization of methods into simple (e.g., filtering, oversampling) and advanced (e.g., frequency-domain outlier tracking).
  • Evaluation of the efficacy and limitations of individual and combined artifact removal strategies.

Main Results:

  • Basic filtering techniques are often insufficient for complete artifact removal.
  • Advanced frequency-domain methods show potential but are underutilized due to complexity.
  • A lack of user-friendly tools and the need for careful parameter tuning are key barriers.
  • An open-source toolbox integrating multiple artifact removal strategies is presented.

Conclusions:

  • Effective removal of DBS artifacts from EEG is critical for advancing neuromodulation research.
  • Combining various signal processing techniques, facilitated by accessible tools, is essential.
  • The highlighted open-source toolbox offers a practical solution for researchers to improve EEG analysis in DBS patients.