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Related Experiment Video

Updated: Jun 25, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Electromyogenic artifacts and electroencephalographic inferences.

Alexander J Shackman1, Brenton W McMenamin, Heleen A Slagter

  • 1Laboratory for Affective Neuroscience and Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, W.J. Brogden Hall, 1202 West Johnson Street, Madison, WI 53706, USA. shackman@wisc.edu

Brain Topography
|February 14, 2009
PubMed
Summary
This summary is machine-generated.

Electromyogenic (EMG) artifact contaminates electroencephalography (EEG) data. This study validates two correction methods, finding the general linear model (GLM) effective for ongoing spectral changes, while temporal independent component analysis (ICA) requires further research.

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Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
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Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Published on: March 19, 2021

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Muscle artifact (electromyogenic artifact, EMG) presents a significant challenge to the validity of electroencephalography (EEG) research, particularly in frequency-domain analyses.
  • EMG's high amplitude and broad spectrum can obscure neural signals, even at low frequencies like the alpha band.
  • Existing EMG correction techniques often lack rigorous validation, questioning their practical utility.

Purpose of the Study:

  • To quantitatively evaluate the validity of two prevalent EMG correction techniques: general linear model (GLM) and temporal independent component analysis (ICA).
  • To assess the effectiveness of these methods in addressing EMG contamination in EEG data.

Main Methods:

  • Review and quantitative investigation of laboratory's recent work on EMG correction.
  • Validation of GLM-based methods for correcting ongoing or induced spectral changes.
  • Preliminary assessment of temporal ICA for EMG artifact removal.

Main Results:

  • Intra-individual GLM-based methods demonstrated sensitivity and specificity for correcting ongoing or induced spectral changes in EEG.
  • GLM methods were found ineffective for evoked (phase-locked) or source-localized spectral changes.
  • Initial findings suggest temporal ICA may not be a complete solution for EMG contamination, warranting further investigation.

Conclusions:

  • GLM-based approaches offer a valuable tool for specific types of EMG artifact correction in EEG.
  • Temporal ICA shows potential but requires additional research to determine its full capabilities and limitations.
  • Emerging methodological trends promise significant advancements for both basic and applied EEG research.