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Suppressing Non-Stationary Motion Artefacts in Mobile EEG Using Generalized Eigenvalue Decomposition.

Mohammad Khazaei1, Khadijeh Raeisi1, Patrique Fiedler2,3

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This study introduces a new method for removing motion artifacts in mobile electroencephalography (EEG) data, improving brain signal analysis during complex movements like sports. The generalized eigenvalue decomposition (GED) approach effectively suppresses artifacts, enhancing data quality for real-world brain activity research.

Keywords:
automatic artefact removalgeneralized eigenvalue decompositionmobile EEGmotion artefacts

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Mobile electroencephalography (EEG) offers insights into brain activity during real-world behaviors.
  • Existing motion artifact removal methods struggle with highly variable and high-speed movements, limiting EEG applications in dynamic activities like sports.

Purpose of the Study:

  • To develop and validate a novel method for identifying and suppressing transient, non-periodic motion artifacts in mobile EEG data.
  • To enhance the interpretability of EEG signals during activities involving rapid, free-body movements, such as sports practice.

Main Methods:

  • A method based on generalized eigenvalue decomposition (GED) was developed to isolate and remove motion-related artifacts.
  • The approach leverages covariance matrices from artefactual and resting-state EEG segments for artifact component identification.
  • Multichannel EEG signal reconstruction was performed after artifact component removal.

Main Results:

  • The proposed GED-based method demonstrated superior performance compared to state-of-the-art techniques in terms of signal-to-error ratio (SER) and artifact-to-residue ratio (ARR).
  • The method effectively preserved brain spectral power and showed reduced computation time.
  • Validation on datasets with stereotyped and dynamic movements, including table tennis, confirmed its efficacy.

Conclusions:

  • The developed method offers a robust and generalizable solution for motion artifact suppression in mobile EEG.
  • This approach is particularly valuable for extreme recording conditions, such as during active sports, enabling more reliable brain activity analysis.