Muscle Artifact Removal in Ictal Scalp-EEG Based on Blind Source Separation

Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. These artifacts obscure the EEG and complicate its interpretation or even make the interpretation unfeasible. This paper focuses on the particular context of extraction of low-voltage rapid ictal discharges from ictal scalp-EEG activity cantaminated by muscle artifact. In this context our aim was to evaluate the ability of Independent Component Analysis (ICA) and Canonical Correlation Analysis (CCA), to remove muscle artefacts from surface EEG signals. The efficiency of ICA and CCA to correct the muscular artifact was evaluated both on simulated data and on real data recorded in an epileptic patient. The obtained results show that some ICA methods and CCA removed successfully the muscle artifact without altering the recorded underlying ictal activity.

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

[2]  Christian Jutten,et al.  Space or time adaptive signal processing by neural network models , 1987 .

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

[4]  G. Alarcón,et al.  Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. , 1995, Electroencephalography and clinical neurophysiology.

[5]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources , 1999, Neural Comput..

[6]  Terrence J. Sejnowski,et al.  Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources , 1999, Neural Computation.

[7]  Hans Knutsson,et al.  Exploratory fMRI Analysis by Autocorrelation Maximization , 2002, NeuroImage.

[8]  P. LeVan,et al.  Independent Component Analysis in the Study of Focal Seizures , 2006, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[9]  W. van Paesschen,et al.  Improving the Interpretation of Ictal Scalp EEG: BSS–CCA Algorithm for Muscle Artifact Removal , 2007, Epilepsia.

[10]  Fabrice Wendling,et al.  A Physiologically Plausible Spatio-Temporal Model for EEG Signals Recorded With Intracerebral Electrodes in Human Partial Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.

[11]  Lotfi Senhadji,et al.  Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches , 2012, EURASIP J. Adv. Signal Process..

[12]  Lotfi Senhadji,et al.  ICA-based EEG denoising: a comparative analysis of fifteen methods , 2012 .