Detection and Removal of Muscle Artifacts from Scalp EEG Recordings in Patients with Epilepsy

The Electroencephalogram (EEG) is often contaminated by muscle artifacts. EEG is a widely used recording technique for the study of many brain related diseases such as epilepsy. The detection and removal of muscle artifacts from the EEG signal poses a real challenge and is crucial for the reliable interpretation of EEG-based quantitative measures. In this paper, an automatic method for detection and removal of muscle artifacts from scalp EEG recordings, based on canonical correlation analysis (CCA), is introduced. To this end we exploit the fact that the EEG signal may exhibit altered autocorrelation structure and spectral characteristics during periods when it is contaminated by muscle activity. Therefore, we design classifiers in order to automatically discriminate between contaminated and non-contaminated EEG epochs using features based on the aforementioned quantities and examine their performance on simulated data and in scalp EEG recordings obtained from patients with epilepsy.

[1]  T. Jung,et al.  Tonic, phasic, and transient EEG correlates of auditory awareness in drowsiness. , 1996, Brain research. Cognitive brain research.

[2]  Andreas Ziehe,et al.  TDSEP { an e(cid:14)cient algorithm for blind separation using time structure , 1998 .

[3]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[4]  Anja Vogler,et al.  An Introduction to Multivariate Statistical Analysis , 2004 .

[5]  Slawomir J. Nasuto,et al.  Comparison of Temporal and Standard Independent Component Analysis (ICA) Algorithms for EEG Analysis , 2003 .

[6]  Pierre Perruchet,et al.  The exploitation of distributional information in syllable processing , 2004, Journal of Neurolinguistics.

[7]  V. A. Makarov,et al.  Recovering EEG brain signals: Artifact suppression with wavelet enhanced independent component analysis , 2006, Journal of Neuroscience Methods.

[8]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[9]  Deng Cai,et al.  Unsupervised feature selection for multi-cluster data , 2010, KDD.

[10]  T. W. Anderson,et al.  An Introduction to Multivariate Statistical Analysis , 1959 .

[11]  J. Wolpaw,et al.  EMG contamination of EEG: spectral and topographical characteristics , 2003, Clinical Neurophysiology.

[12]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[13]  Martin J. McKeown,et al.  Removing electroencephalographic artifacts: comparison between ICA and PCA , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

[14]  Wim Van Paesschen,et al.  Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram , 2006, IEEE Transactions on Biomedical Engineering.

[15]  P. Comon.  Independent Component Analysis , 1992 .

[16]  Seán F. McLoone,et al.  The Use of Ensemble Empirical Mode Decomposition With Canonical Correlation Analysis as a Novel Artifact Removal Technique , 2013, IEEE Transactions on Biomedical Engineering.

[17]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[18]  J. Martinerie,et al.  Anticipation of epileptic seizures from standard EEG recordings , 2001, The Lancet.

[19]  R. Kass,et al.  Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. , 2004, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[20]  R. Kass,et al.  Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods , 2004 .

[21]  R N Vigário,et al.  Extraction of ocular artefacts from EEG using independent component analysis. , 1997, Electroencephalography and clinical neurophysiology.

[22]  Bart Vanrumste,et al.  Validation of ICA as a tool to remove eye movement artifacts from EEG/ERP. , 2010, Psychophysiology.

[23]  J. Martinerie,et al.  Anticipation of epileptic seizures from standard EEG recordings , 2003, The Lancet.