Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging

Hallez, Hans; Vos, Maarten De; Vanrumste, Bart; Hese, Peter Van; Assecondi, Sara; Laere, Koen Van; Dupont, Patrick; Paesschen, Wim Van; Huffel, Sabine Van; Lemahieu, Ignace

doi:10.1016/j.clinph.2009.05.010

Cited by 40 publications

(23 citation statements)

References 23 publications

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“…With this respect, the results are in harmony with those of a recent and important study showing the usefulness of applying ICA/CCA denoising techniques to ictal EEG signals in order to localize the epileptic zones [58]. Compared to this study, our work uses simulations of realistic epileptic EEG signals to quantitatively compare the different denoising algorithms.…”

Section: Discussionsupporting

confidence: 78%

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Safieddine

Kachenoura

Albera

et al. 2012

EURASIP J. Adv. Signal Process.

152

122

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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.

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Section: Discussionsupporting

confidence: 78%

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Safieddine

Kachenoura

Albera

et al. 2012

EURASIP J. Adv. Signal Process.

152

122

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“…However, restricting eye movements/blinks limits experimental designs and may impact cognitive processes under investigation (Joyce et al, 2004). Generally speaking, there are two kinds of strategies for obtaining high-quality EEG recordings (Joyce et al, 2004; Fatourechi et al, 2007; Schlögl et al, 2007; Hallez et al, 2009; Zhou and Gotman, 2009): (1) eliminating contaminated trials after visual inspection, or (2) correcting the artifacts automatically. The former method leads to a substantial loss of data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Wang¹,

Miao²,

Blohm³

2012

Front. Neurosci.

115

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Recent studies show that scalp electroencephalography (EEG) as a non-invasive interface has great potential for brain-computer interfaces (BCIs). However, one factor that has limited practical applications for EEG-based BCI so far is the difficulty to decode brain signals in a reliable and efficient way. This paper proposes a new robust processing framework for decoding of multi-class motor imagery (MI) that is based on five main processing steps. (i) Raw EEG segmentation without the need of visual artifact inspection. (ii) Considering that EEG recordings are often contaminated not just by electrooculography (EOG) but also other types of artifacts, we propose to first implement an automatic artifact correction method that combines regression analysis with independent component analysis for recovering the original source signals. (iii) The significant difference between frequency components based on event-related (de-) synchronization and sample entropy is then used to find non-contiguous discriminating rhythms. After spectral filtering using the discriminating rhythms, a channel selection algorithm is used to select only relevant channels. (iv) Feature vectors are extracted based on the inter-class diversity and time-varying dynamic characteristics of the signals. (v) Finally, a support vector machine is employed for four-class classification. We tested our proposed algorithm on experimental data that was obtained from dataset 2a of BCI competition IV (2008). The overall four-class kappa values (between 0.41 and 0.80) were comparable to other models but without requiring any artifact-contaminated trial removal. The performance showed that multi-class MI tasks can be reliably discriminated using artifact-contaminated EEG recordings from a few channels. This may be a promising avenue for online robust EEG-based BCI applications.

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“…BSS-CCA is another method to filter muscular artifacts [2, 11, 13] and it seems to be one of the most efficient methods. Then, it is interesting to compare the results of this method with those of the proposed approach.…”

Section: Methodsmentioning

confidence: 99%

“…The three following filters were then compared:a standard 1-order low-pass filter at 30 Hz common to many EEG device software applications,a filter achieved with BSS-CCA [10, 13],a DAFOP filter with the above optimized parameters. …”

Section: Methodsmentioning

confidence: 99%

Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study

Boudet

Peyrodie

Szurhaj

et al. 2014

The Scientific World Journal

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Muscle artifacts constitute one of the major problems in electroencephalogram (EEG) examinations, particularly for the diagnosis of epilepsy, where pathological rhythms occur within the same frequency bands as those of artifacts. This paper proposes to use the method dual adaptive filtering by optimal projection (DAFOP) to automatically remove artifacts while preserving true cerebral signals. DAFOP is a two-step method. The first step consists in applying the common spatial pattern (CSP) method to two frequency windows to identify the slowest components which will be considered as cerebral sources. The two frequency windows are defined by optimizing convolutional filters. The second step consists in using a regression method to reconstruct the signal independently within various frequency windows. This method was evaluated by two neurologists on a selection of 114 pages with muscle artifacts, from 20 clinical recordings of awake and sleeping adults, subject to pathological signals and epileptic seizures. A blind comparison was then conducted with the canonical correlation analysis (CCA) method and conventional low-pass filtering at 30 Hz. The filtering rate was 84.3% for muscle artifacts with a 6.4% reduction of cerebral signals even for the fastest waves. DAFOP was found to be significantly more efficient than CCA and 30 Hz filters. The DAFOP method is fast and automatic and can be easily used in clinical EEG recordings.

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Removing muscle and eye artifacts using blind source separation techniques in ictal EEG source imaging

Cited by 40 publications

References 23 publications

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces

Dual Adaptive Filtering by Optimal Projection Applied to Filter Muscle Artifacts on EEG and Comparative Study

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