Brain Topography

, Volume 22, Issue 1, pp 7–12 | Cite as

Electromyogenic Artifacts and Electroencephalographic Inferences

  • Alexander J. Shackman
  • Brenton W. McMenamin
  • Heleen A. Slagter
  • Jeffrey S. Maxwell
  • Lawrence L. Greischar
  • Richard J. Davidson
Original Paper

Abstract

Muscle or electromyogenic (EMG) artifact poses a serious risk to inferential validity for any electroencephalography (EEG) investigation in the frequency-domain owing to its high amplitude, broad spectrum, and sensitivity to psychological processes of interest. Even weak EMG is detectable across the scalp in frequencies as low as the alpha band. Given these hazards, there is substantial interest in developing EMG correction tools. Unfortunately, most published techniques are subjected to only modest validation attempts, rendering their utility questionable. We review recent work by our laboratory quantitatively investigating the validity of two popular EMG correction techniques, one using the general linear model (GLM), the other using temporal independent component analysis (ICA). We show that intra-individual GLM-based methods represent a sensitive and specific tool for correcting on-going or induced, but not evoked (phase-locked) or source-localized, spectral changes. Preliminary work with ICA shows that it may not represent a panacea for EMG contamination, although further scrutiny is strongly warranted. We conclude by describing emerging methodological trends in this area that are likely to have substantial benefits for basic and applied EEG research.

Keywords

EMG EEG ERSP ERS/ERD LORETA source localization Review 

Notes

Acknowledgments

This work was supported by NIMH grants P50-MH52354 and MH43454. We thank David Bachhuber, Bridget Kelly, and Adam Koppenhaver for assistance.

References

  1. Barbati G, Porcaro C, Hadjipapas A, Adjamian P, Pizzella V, Romani GL, Seri S, Tecchio F, Barnes GR (2008) Functional source separation applied to induced visual gamma activity. Hum Brain Mapp 29:131–141PubMedCrossRefGoogle Scholar
  2. Crespo-Garcia M, Atienza M, Cantero JL (2008) Muscle artifact removal from human sleep EEG by using independent component analysis. Ann Biomed Eng 36:467–475PubMedCrossRefGoogle Scholar
  3. De Clercq W, Vergult A, Vanrumste B, Van Hees J, Palmini A, Van Paesschen W, Van Huffel S (2005) A new muscle artifact removal technique to improve the interpretation of the ictal scalp electroencephalogram. Conf Proc IEEE Eng Med Biol Soc 1:944–947PubMedGoogle Scholar
  4. Delorme A, Sejnowski T, Makeig S (2007) Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage 34(4):1443–1449PubMedCrossRefGoogle Scholar
  5. Fatourechi M, Bashashati A, Ward RK, Birch GE (2007) EMG and EOG artifacts in brain computer interface systems: a survey. Clin Neurophysiol 118:480–494PubMedCrossRefGoogle Scholar
  6. Fitzgibbon SP, Powers DMW, Pope KJ, Clark CR (2007) Removal of EEG noise and artifact using blind source separation. J Clin Neurophysiol 24:232–243PubMedCrossRefGoogle Scholar
  7. Goncharova II, McFarland DJ, Vaughan TM, Wolpaw JR (2003) EMG contamination of EEG: spectral and topographical characteristics. Clin Neurophysiol 114:1580–1593PubMedCrossRefGoogle Scholar
  8. Grouiller F, Vercueil L, Krainik A, Segebarth C, Kahane P, David O (2007) A comparative study of different artefact removal algorithms for EEG signals acquired during functional MRI. Neuroimage 38:124–137PubMedCrossRefGoogle Scholar
  9. Hu D, Yan L, Liu Y, Zhou Z, Friston KJ, Tan C, Wu D (2005) Unified SPM-ICA for fMRI analysis. Neuroimage 25:746–755PubMedCrossRefGoogle Scholar
  10. Li Y-O, Adal T, Calhoun VD (2008) Estimating the number of independent components for functional magnetic resonance imaging data. Hum Brain Mapp 28:1251–1266CrossRefGoogle Scholar
  11. Lutz A, Greischar LL, Rawlings NB, Ricard M, Davidson RJ (2004) Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. Proc Natl Acad Sci USA 101(46):16369–16373PubMedCrossRefGoogle Scholar
  12. Makeig S, Debener S, Onton J, Delorme A (2004) Mining event-related brain dynamics. Trends Cogn Sci 8:204–210PubMedCrossRefGoogle Scholar
  13. Mammone N, Morabito FC (2008) Enhanced automatic artifact detection based on independent component analysis and Renyi’s entropy. Neural Netw 21:1029–1040PubMedCrossRefGoogle Scholar
  14. McMenamin BW, Shackman AJ, Maxwell JS, Greischar LL, Davidson RJ. (in press). Validation of regression-based myogenic correction techniques for scalp and source-localized EEG. PsychophysiologyGoogle Scholar
  15. Pizzagalli DA (2007) Electroencephalography and high-density electrophysiological source localization. In: Cacioppo JT, Tassinary LG, Berntson GG (eds) Handbook of psychophysiology. Cambridge University Press, NY, pp 56–84Google Scholar
  16. Seaman MA, Serlin RC (1998) Equivalence confidence intervals for two-group comparisons of means. Psychol Methods 3(4):403–411CrossRefGoogle Scholar
  17. Tassinary LG, Cacioppo JT, Vanman EJ (2007) The skeletomotor system: surface electromyography. In: Cacioppo JT, Tassinary LG, Berntson GG (eds) Handbook of psychophysiology, 3rd edn. Cambridge University Press, NY, pp 267–302Google Scholar
  18. Whitham EM, Pope KJ, Fitzgibbon SP, Lewis T, Clark CR, Loveless S, Broberg M, Wallace A, DeLosAngeles D, Lillie P, Hardy A, Fronsko R, Pulbrook A, Willoughby JO (2007) Scalp electrical recording during paralysis: quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG. Clin Neurophysiol 118(8):1877–1888PubMedCrossRefGoogle Scholar
  19. Whitham EM, Lewis T, Pope KJ, Fitzgibbon SP, Clark CR, Loveless S, DeLosAngeles D, Wallace AK, Broberg M, Willoughby JO (2008) Thinking activates EMG in scalp electrical recordings. Clin Neurophysiol 119(5):1166–1175PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Alexander J. Shackman
    • 1
  • Brenton W. McMenamin
    • 2
  • Heleen A. Slagter
    • 1
  • Jeffrey S. Maxwell
    • 3
  • Lawrence L. Greischar
    • 1
  • Richard J. Davidson
    • 1
    • 4
  1. 1.Laboratory for Affective Neuroscience and Waisman Laboratory for Brain Imaging and BehaviorUniversity of WisconsinMadisonUSA
  2. 2.Center for Cognitive SciencesUniversity of MinnesotaTwin CitiesUSA
  3. 3.U.S. Army Research LaboratoryAberdeen Proving GroundAberdeenUSA
  4. 4.Health Emotions Research InstituteUniversity of WisconsinMadisonUSA

Personalised recommendations