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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


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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  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.


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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 The proposed automatization can be obtained after sending an email to the corresponding author.

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Correspondence to De Maarten Vos.

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An erratum to this article can be found at

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:

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

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 \(\textbf{B}\):

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

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:

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

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:

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

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).

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