Abstract
Abstract Electroencephalographic (EEG) recordings are often contaminated with muscle artifacts. This disturbing myogenic activity not only strongly affects the visual analysis of EEG, but also most surely impairs the results of EEG signal processing tools such as source localization. This article focuses on the particular context of the contamination epileptic signals (interictal spikes) by muscle artifact, as EEG is a key diagnosis tool for this pathology. In this context, our aim was to compare the ability of two stochastic approaches of blind source separation, namely independent component analysis (ICA) and canonical correlation analysis (CCA), and of two deterministic approaches namely empirical mode decomposition (EMD) and wavelet transform (WT) to remove muscle artifacts from EEG signals. To quantitatively compare the performance of these four algorithms, epileptic spike-like EEG signals were simulated from two different source configurations and artificially contaminated with different levels of real EEG-recorded myogenic activity. The efficiency of CCA, ICA, EMD, and WT to correct the muscular artifact was evaluated both by calculating the normalized mean-squared error between denoised and original signals and by comparing the results of source localization obtained from artifact-free as well as noisy signals, before and after artifact correction. Tests on real data recorded in an epileptic patient are also presented. The results obtained in the context of simulations and real data show that EMD outperformed the three other algorithms for the denoising of data highly contaminated by muscular activity. For less noisy data, and when spikes arose from a single cortical source, the myogenic artifact was best corrected with CCA and ICA. Otherwise when spikes originated from two distinct sources, either EMD or ICA offered the most reliable denoising result for highly noisy data, while WT offered the better denoising result for less noisy data. These results suggest that the performance of muscle artifact correction methods strongly depend on the level of data contamination, and of the source configuration underlying EEG signals. Eventually, some insights into the numerical complexity of these four algorithms are given.
Highlights
Electroencephalographic (EEG) recordings are mandatory for the diagnosis of epilepsy
Results on simulated data we report the behavior of Contrast Maximization 2 (CoM2), canonical correlation analysis (CCA), 2T-empirical mode decomposition (EMD), and Discrete Wavelet Transform (DWT) algorithms as a function of signal-to-noise ratio (SNR) in noisy simulated data obtained either from a single epileptic patch or from two patches
2T-EMD does not retrieve the proper spike amplitude at T3 but does not either increase the normalized mean-squared error (NMSE) For data simulated from a single epileptic patch (Figure 2), the calculation, for a set of 50 trials, of the mean performance criterion (NMSE) calculated for all electrodes shows that 2T-EMD: (i) performs better than CoM2, CCA for very low SNR (–30 dB), (ii) gives comparable results with CoM2 and CCA in the case of SNR –25, and (iii) is less efficient than CCA and CoM2 for SNR ranging from –20 to –5 dB
Summary
Electroencephalographic (EEG) recordings are mandatory for the diagnosis of epilepsy. As part of the presurgical evaluation of drug-resistant epilepsy, long-term VideoEEG recordings are performed. On these traces, transient events called interictal spikes occur between seizures, and convey essential information both to guide further explorations such as intracerebral implantation and to assist surgery. Epochs of EEG signals containing spikes have to be free of artifacts when both qualitative and quantitative analyses such as source localization are planned. Muscular or myogenic activity arising from the contraction of head muscles can strongly obscure EEG signals. As recently reviewed in [1], the perturbation induced by this type of artifact is difficult to correct because myogenic activity is of high amplitude (possibly several times larger than the EEG signal), wide spectral distribution, and variable topographical distribution
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