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Removal of Muscle Artifacts from EEG Recordings of Spoken Language Production

An Erratum to this article was published on 09 June 2010

Abstract

Research on the neural basis of language processing has often avoided investigating spoken language production by fear of the electromyographic (EMG) artifacts that articulation induces on the electro-encephalogram (EEG) signal. Indeed, such articulation artifacts are typically much larger than the brain signal of interest. Recently, a Blind Source Separation technique based on Canonical Correlation Analysis was proposed to separate tonic muscle artifacts from continuous EEG recordings in epilepsy. In this paper, we show how the same algorithm can be adapted to remove the short EMG bursts due to articulation on every trial. Several analyses indicate that this method accurately attenuates the muscle contamination on the EEG recordings, providing to the neurolinguistic community a powerful tool to investigate the brain processes at play during overt language production.

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Notes

  1. Similar applications of BSS-CCA to other signals can be found in Hardoon et al. (2004), De Vos et al. (2007).

  2. We use (:) to denote that all channels are involved.

References

  • Alario, F.-X., & Ferrand, L. (1999). A set of 400 pictures standardized for french: Norms for name agreement, image agreement, familiarity, visual complexity, image variability, and age of acquisition. Behavior Research Methods, Instruments & Computers, 31(3), 531–552.

    CAS  Google Scholar 

  • Alario, F.-X., Ferrand, L., Laganaro, M., New, B., Frauenfelder, U. H., & Segui, J. (2004). Predictors of picture naming speed. Behavior Research Methods Instruments & Computers, 36, 140–155.

    Google Scholar 

  • Borga, M., & Knutsson, H. (2001). A canonical correlation approach to blind source separation. Tech. Rep. LiU-IMT-EX-0062, Dept. of Biomedical Engineering, Linköping University, Sweden.

  • Brooker, B. H., & Donald, M. W. (1980). Contribution of the speech musculature to apparent human eeg asymmetries prior to vocalization. Brain and Language, 9, 226–245.

    Article  CAS  PubMed  Google Scholar 

  • Chan, A., Englehart, K., Hudgins, B., & Lovely, D. (2002). Hidden markov model classification of myoelectric signals in speech. IEEE Engineering in Medicine and Biology Magazine, 21, 143–146.

    Article  CAS  PubMed  Google Scholar 

  • Comon, P. (1994). Independent component analysis, a new concept? Signal Process, 36, 287–314.

    Article  Google Scholar 

  • Comon, P., & Jutten, C. (2010). Handbook of blind source separation, independent component analysis and applications. Academic Press.

  • Conway, B. A., Halliday, D. M., Farmer, S. F., Shahani, U., Maas, P., Weir, A. I., et al. (1995). Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man. Journal of Physiology (London), 489(3), 917–924.

    CAS  Google Scholar 

  • Crespo-Garcia, M., Atienza, M., & Cantero, J. (2008). Muscle artifact removal from human sleep EEG by using independent component analysis. Journal of Biomedical Engineering, 36, 467–475.

    Article  Google Scholar 

  • De Clercq, W., Vergult, A., Vanrumste, B., Van Paesschen, W., & Van Huffel, S. (2006). Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Transactions on Biomedical Engineering, 53, 2583–2587.

    Article  PubMed  Google Scholar 

  • De Vos, M., Laudadio, T., Simonetti, A., Heerschap, A., & Van Huffel, S. (2007). Fast nosologic imaging of the brain. Journal of Magnetic Resonance, 184, 292–301.

    Article  PubMed  Google Scholar 

  • Friedman, B. H., & Thayer, J. F. (1991). Facial muscle activity and eeg recordings: Redundancy analysis. Electroencephalography and clinical Neurophysiology, 79, 358–360.

    CAS  Google Scholar 

  • Friman, O., Cedefamn, J., Lundberg, P., Borga, M., & Knutsson, H. (2001). Detection of neural activity in functional MRI using canonical correlation analysis. Magnetic Resonance in Medicine, 45, 323–330.

    Article  CAS  PubMed  Google Scholar 

  • Ganushchak, L. Y., & Schiller, N. O. (2008). Motivation and semantic context affect brain error-monitoring activity: An event-related brain potentials study. NeuroImage, 39, 395–405.

    Article  PubMed  Google Scholar 

  • Glaser, W. R. (1992). Picture naming. Cognition, 42, 61–105.

    Article  CAS  PubMed  Google Scholar 

  • Golub, G., Van Loan, C. F. (1996). Matrix computations (3rd ed.). Baltimore: John Hopkins University Press.

    Google Scholar 

  • Goncharova, I. I., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2003). Emg contamination of eeg: Spectral and topographical characteristics. Clinical Neurophysiology, 114(9), 1580–1593.

    Article  CAS  PubMed  Google Scholar 

  • Gratton, G., Coles, M., & Donchin, E. (1983). A new method for offline removal of ocular artifacts. Electroencephalography and Clinical Neurophysiology, 55, 468–484.

    Article  CAS  PubMed  Google Scholar 

  • Hansen, P. C., & Jensen, S. H. (1998). Fir filter representations of reduced-rank noise reduction. IEEE Transactions on Signal Processing, 46, 1737–1741.

    Article  Google Scholar 

  • Hardoon, D., Szedmak, S., & Shawe-Taylor, J. (2004). Canonical correlation analysis: An overview with application to learning methods. Neural Computation, 16, 2639–2664.

    Article  PubMed  Google Scholar 

  • Hotelling, H. (1936). Relations between two sets of variates. Biometrika, 28, 321–377.

    Google Scholar 

  • Indefrey, P., & Levelt, W. J. M. (2004). The spatial and temporal signatures of word production components. Cognition, 92(1), 101–144.

    Article  CAS  PubMed  Google Scholar 

  • Jescheniak, J. D., Schriefers, H., Garrett, M. F., & Friederici, A. D. (2002). Exploring the activation of semantic and phonological codes during speech planning with event-related brain potentials. Journal of Cognitive Neuroscience, 14(6), 951–964.

    Article  PubMed  Google Scholar 

  • Jung, T., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., Sejnowski, T. (2001). Analysis and visualization of single-trial event-related potentials. Human Brain Mapping, 14, 166–85.

    Article  CAS  PubMed  Google Scholar 

  • Luck, S. L. (2005). An introduction to the event-related potential technique. MIT Press.

    Google Scholar 

  • Masaki, H., Tanaka, H., Takasawa, N., & Yamazaki, K. (2001). Error-related brain potentials elicited by vocal errors. Neuroreport: For Rapid Communication of Neuroscience Research, 12(9), 1851–1855.

    CAS  Google Scholar 

  • McAdam, D. W., & Whitaker, H. A. (1971). Language production: Electroencephalographic localization in the normal human brain. Science, 172(3982), 499–502.

    Article  CAS  PubMed  Google Scholar 

  • McMenamin, B., Shackman, A., Maxwell, J., Bachhuber, D., Koppenhaver, A., Greischar, L., et al. (2010). Validation of ica-based myogenic artifact correction for scalp and source-localized EEG. Neuroimage, 49, 2416–2432.

    Article  PubMed  Google Scholar 

  • McMenamin, B., Shackman, A., Maxwell, J., Greischar, L., & Davidson, R. (2009). Validation of regression-based myogenic correction techniques for scalp and source-localized EEG. Psychophysiology, 46, 578–592.

    Article  PubMed  Google Scholar 

  • Mima, T., & Hallett, M. (1999). Corticomuscular coherence: a review. Journal of Clinical Neurophysiology, 16(6), 501–511.

    Article  CAS  PubMed  Google Scholar 

  • Morrell, L. K., Huntington, D. A., McAdam, D. W., & Whitaker, H. A. (1971). Electrocortical localization of language production. Science, 174(4016), 1359–1360.

    Article  CAS  PubMed  Google Scholar 

  • Osterhout, L., McLaughlin, J., & Bersick, M. (1997). Event-related brain potentials and human language. Trends in Cognitive Sciences, 1, 203–209.

    Article  Google Scholar 

  • Protopapas, A. (2007). Checkvocal: A program to facilitate checking the accuracy and response time of vocal responses from dmdx. Behavior Research Methods, 39, 859–862.

    PubMed  Google Scholar 

  • Riès, S., Janssen, N., Dufau, S., Alario, F.-X., & Burle, B. (2010). General purpose monitoring during speech production. Journal of Cognitive Neuroscience (in press).

  • Shackman, A., McMenamin, B., Slagter, H., Maxwell, J., Greischar, L., & Davidson, R. (2009). Electromyogenic artifacts and electroencephalographic inferences. Brain Topography, 22, 7–12.

    Article  PubMed  Google Scholar 

  • Stemmer, B., & Whitaker, H. (2008). Handbook of the neuroscience of language. Academic Press.

  • Urrestarazu, E., Iriarte, J., Alegre, M., Valencia, M., Viteri, C., & Artieda, J. (2004). Independent component analysis removing artifacts in ictal recordings. Epilepsia, 45(9), 1071–1078.

    Article  PubMed  Google Scholar 

  • van Turennout, M., Hagoort, P., & Brown, C. M. (1998). Brain activity during speaking: From syntax to phonology in 40 milliseconds. Science, 280, 572–574.

    Article  CAS  PubMed  Google Scholar 

  • Vergult, A., De Clercq, W., Palmini, A., Vanrumste, B., Dupont, P., Van Huffel, S., et al. (2007). Improving the interpretation of ictal scalp eeg: Bss-cca algorithm for muscle artifact removal. Epilepsia, 48, 950–958.

    Article  PubMed  Google Scholar 

  • Weidong, Z., & Gotman, J. (2004). Removal of emg and ecg artifacts from eeg based on wavelet transform and ica. In 26th annual international conference of the engineering in medicine and biology society (Vol. 1, pp. 392–395).

  • Whitham, E. M., Pope, K., Fitzgibbon, S. L. T., Clark, C., Loveless, S., Broberg, M. A. W., et al. (2007). Scalp electrical recording during paralysis: Quantitative evidence that EEG frequencies above 20 Hz are contaminated by EMG. Clinical Neurophysiology, 118, 1877–1888.

    Article  PubMed  Google Scholar 

  • Zarzoso, V., & Comon, P. (2008). Robust independent component analysis for blind source separation and extraction with application in electrocardiography. In 30th annual international conference of the ieee engineering in medicine and biology society (IEEE EMBS ’08) (pp. 3344–3347). Vancouver, Canada.

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Acknowledgements

This research is funded by a PhD grant of the Institute for the Promotion of Innovation through Science and Technology in Flanders (IWT-Vlaanderen)and a doctoral grant for the French ministry of research; Research supported by ANR-07-JCJC-0074; Research Council KUL: GOA-AMBioRICS, GOA-MANET, CoE EF/05/006 Optimization in Engineering (OPTEC), IDO 05/010 EEG-fMRI, IOF-KP06/11 FunCopt, several PhD/postdoc & fellow grants; Flemish Government: FWO: PhD/postdoc grants, projects, G.0407.02 (support vector machines), G.0360.05 (EEG, Epileptic), G.0519.06 (Noninvasive brain oxygenation), G.0321.06 (Tensors/Spectral Analysis), G.0302.07 (SVM), G.0341.07 (Data fusion), G.0427.10N (Integrated EEG-fMRI), research communities (ICCoS, ANMMM); IWT: TBM070713-Accelero, TBM-IOTA3; Belgian Federal Science Policy Of\/f\/ice IUAP P6/04 (DYSCO, ‘Dynamical systems, control and optimization’, 2007–2011); EU: BIOPATTERN (FP6-2002-IST 508803), ETUMOUR (FP6-2002-LIFESCIHEALTH 503094), FAST (FP6-MC-RTN-035801), Neuromath (COST-BM0601) ESA: Cardiovascular Control (Prodex-8 C90242), European Research Council under the European Community’s Seventh Framework Programme (FP7/2007-2013 Grant Agreement no. 241077)

Information Sharing Statement

The original BSS-CCA method is available at www.neurology-kuleuven.be/index.php?id=210. The proposed automatization can be obtained after sending an email to the corresponding author.

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Correspondence to De Maarten Vos.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s12021-010-9076-8

Appendix: CCA

Appendix: CCA

Ordinary correlation analysis quantifies the relation between (realizations of) two variables a(t) and b(t) by means of a correlation coefficient ρ:

$$ \rho=\frac{Cov[{\bf a},{\bf b}]}{\sqrt{V[{\bf a}]V[{\bf b}]}} \label{eq:correlatie} $$
(6)

in which Cov and V indicate respectively the co—and variance. CCA is a multivariate extension of ordinary correlation analysis.

Consider 2 multivariate zero-mean vectors \(\textbf{A}\) and \(\textbf{B}\), and two new scalar variables, \(\tilde{\bf a}\) and \(\tilde{\bf b}\), generated as linear combinations of the components in \(\textbf{A}\) and \(\textbf{B}\):

$$ \begin{array}{lll} \textbf{A}&=&[{\bf a}_1(t), \ldots , {\bf a}_m(t)]^T\\ \textbf{B}&=&[{\bf b}_1(t), \ldots , {\bf b}_n(t)]^T, t=1, .., N\\ \tilde{\bf a} &=& w_{{\bf a}_{1}}{\bf a}_1 + \ldots + w_{{\bf a}_{m}}{\bf a}_m = \textbf{w}_{\bf a}^T \textbf{A} \\ \label{eq:y}\tilde{\bf b} &=& w_{{\bf b}_{1}} {\bf b}_1 + \ldots + w_{{\bf b}_{m}} {\bf b}_m = \textbf{w}_{\bf b}^T\textbf{B} \end{array} $$
(7)

CCA computes the coefficients \(\textbf{w}_{\bf a}\) and \(\textbf{w}_{\bf b}\) that maximize the correlation between \(\tilde{\bf a}\) and \(\tilde{\bf b}\). These coefficients are the regression weights and \(\tilde{\bf a}\) and \(\tilde{\bf b}\) are denoted as canonical variates. The resulting correlation coefficient is the canonical correlation coefficient.

It can be shown that finding these regression weights correspond to solving an eigen value problem.

By inserting Eq. 7 into the definition of the correlation coefficient (6), and assuming the means of \(\textbf{A}\) and \(\textbf{B}\) zero, we obtain:

$$ \rho = \frac{\textbf{w}_{\bf a}^T \textbf{C}_{{\bf a}{\bf b}} \textbf{w}_{\bf b}}{\sqrt{( \textbf{w}_{\bf a}^T \textbf{C}_{{\bf a}{\bf a}} \textbf{w}_{\bf a} ) (\textbf{w}_{\bf b}^T \textbf{C}_{{\bf b}{\bf b}} \textbf{w}_{\bf b}) }} $$
(8)

with \(\textbf{C}_{{\bf a}{\bf a}}\) and \(\textbf{C}_{{\bf b}{\bf b}}\) the variance matrices from respectively \(\textbf{A}\) and \(\textbf{B}\) and \(\textbf{C}_{{\bf a}{\bf b}}\) the covariance matrix from \(\textbf{A}\) and \(\textbf{B}\). ρ is a function of \(\textbf{w}_{\bf a}\) and \(\textbf{w}_{\bf b}\). In order to maximise the correlation coefficients , we impose the partial derivatives with respect to \(\textbf{w}_{\bf a}\) and \(\textbf{w}_{\bf b}\) to be zero. This results in following system:

$$ \begin{array}{lll} \label{ccaoplossing} \textbf{C}_{{\bf a}{\bf a}}^{-1} \textbf{C}_{{\bf a}{\bf b}} \textbf{C}_{{\bf b}{\bf b}}^{-1} \textbf{C}_{{\bf b}{\bf a}} \textbf{w}_{{\bf a}_i}= \rho ^2 \textbf{w}_{{\bf a}_i} \\ \textbf{C}_{{\bf b}{\bf b}}^{-1} \textbf{C}_{{\bf b}{\bf a}} \textbf{C}_{{\bf a}{\bf a}}^{-1} \textbf{C}_{{\bf a}{\bf b}} \textbf{w}_{{\bf b}_i}= \rho ^2 \textbf{w}_{b_i} \end{array} $$
(9)

This system is an eigenvalue decomposition. The matrices \(\textbf{C}_{{\bf a}{\bf a}}^{-1} \textbf{C}_{{\bf a}{\bf b}} \textbf{C}_{{\bf b}{\bf b}}^{-1} \textbf{C}_{{\bf b}{\bf a}}\) and \(\textbf{C}_{{\bf b}{\bf b}}^{-1} \textbf{C}_{{\bf b}{\bf a}} \textbf{C}_{{\bf a}{\bf a}}^{-1} \textbf{C}_{{\bf a}{\bf b}}\) have the same eigenvalues. The vectors \(\textbf{w}_{\bf a}\) and \(\textbf{w}_{\bf b}\) we are looking for, are the eigenvectors corresponding to the highest eigenvalue. This eigenvalue is the square of the maximal correlation between the canonical variates.

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Vos, D., Riès, S., Vanderperren, K. et al. Removal of Muscle Artifacts from EEG Recordings of Spoken Language Production. Neuroinform 8, 135–150 (2010). https://doi.org/10.1007/s12021-010-9071-0

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  • DOI: https://doi.org/10.1007/s12021-010-9071-0

Keywords

  • EEG
  • ERP
  • EMG
  • Artifact
  • BSS
  • Speech production