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Source-based artifact-rejection techniques for TMS-EEG.

Tuomas P Mutanen1, Johanna Metsomaa2, Matilda Makkonen1

  • 1Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Finland.

Journal of Neuroscience Methods
|September 3, 2022
PubMed
Summary
This summary is machine-generated.

This review focuses on two source-based artifact-rejection techniques for Transcranial Magnetic Stimulation-Electroencephalography (TMS-EEG) analysis: SSP-SIR and SOUND. These methods help distinguish genuine neural activity from noise and artifacts, enabling more reliable data interpretation.

Keywords:
Artifact signalElectroencephalographyNoiseSignal spaceSource spaceTranscranial magnetic stimulation

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Area of Science:

  • Neuroscience
  • Biophysics
  • Signal Processing

Background:

  • Electroencephalography (EEG) signals originate from cortical postsynaptic currents.
  • The head's conductivity smears spatial patterns in EEG, complicating source localization.
  • Distinguishing true Transcranial Magnetic Stimulation (TMS)-evoked cortical activity from noise and artifacts is crucial for accurate analysis.

Purpose of the Study:

  • To review two source-based artifact-rejection techniques for TMS-EEG data: Signal-Space-Projection-Source-Informed Reconstruction (SSP-SIR) and the Source-Estimate-Utilizing Noise-Discarding algorithm (SOUND).
  • To provide practical insights into effectively using these methods for reliable TMS-EEG analysis.
  • To discuss objective quantification of overcorrection and non-biased comparisons between cleaned datasets.

Main Methods:

  • Focus on SSP-SIR for rejecting TMS-evoked muscle artifacts.
  • Focus on SOUND for suppressing general EEG and Magnetoencephalography (MEG) noise signals.
  • Discuss theoretical background and practical application of both methods.

Main Results:

  • Source-based methods allow objective quantification of overcorrection introduced by noise-cleaning algorithms.
  • These techniques enable reliable comparisons between cleaned datasets by accounting for potential signal compromise.
  • The methods are grounded in electrophysiological and anatomical understanding, without requiring statistical independence assumptions for noise.

Conclusions:

  • Source-based cleaning methods are valuable preprocessing tools for TMS-EEG analysis.
  • Objective performance evaluation and accounting for overcorrection ensure reliable data interpretation.
  • The presented theoretical and practical aspects are applicable to broader EEG/MEG research.