Comparative evaluation of existing and new methods for correcting ocular artifacts in electroencephalographic recordings

Kirkove, Murielle; François, Clémentine; Verly, Jacques

doi:10.1016/j.sigpro.2013.11.015

Cited by 26 publications

(20 citation statements)

References 24 publications

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“…The window segments that contained the eye blink artefacts were automatically rejected based on the method shown in [17,18]. A DWT (Discrete Wavelet Transform) was applied with Haar mother wavelet as it resembles eye blink artefacts.…”

Section: Eog Artefacts Removalmentioning

confidence: 99%

A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain–Computer Interface Task Onset

Song

Sepúlveda

2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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Electromyography artifacts are a well-known problem in electroencephalography studies [brain-computer interfaces (BCIs), brain mapping, and clinical areas]. Blind source separation (BSS) techniques are commonly used to handle artifacts. However, these may remove not only the EMG artifacts but also some useful electroencephalography (EEG) sources. To reduce this useful information loss, we propose a new technique for statistically selecting EEG channels that are contaminated with class-dependent EMG (henceforth called EMG-CCh). The EMG-CCh is selected based on the correlation between EEG and facial EMG channels. They were compared (using a Wilcoxon test) to determine whether the artifacts played a significant role in class separation. To ensure that the promising results are not due to the weak EMG removal, reliability tests were done In our data set, the comparison results between BSS artifact removal applied in two ways, to all channels and only to EMG-CCh showed that ICA, PCA, and BSS-CCA can yield significantly better ( ) class separation with the proposed method (79% of the cases for ICA, 53% for PCA, and 11% for BSS-CCA). With BCI competition data, we saw improvement in 60% of the cases for ICA and BSS-CCA. The simple method proposed in this paper showed improvement in class separation with both our data and the BCI competition data. There are no existing methods for removing EMG artifacts based on the correlation between the EEG and EMG channels. Also, the EMG-CCh selection can be used on its own or it can be combined with pre-existing artifact handling methods. For these reasons, we believe that this method can be useful for other EEG studies.

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Section: Eog Artefacts Removalmentioning

confidence: 99%

A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain–Computer Interface Task Onset

Song

Sepúlveda

2018

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…A confusion matrix generally involves the following parameters: The following aggregate metrics can then be calculated based on the above parameters. The criteria for performance evaluation are usually employed in biomedical studies, which include three parts: sensitivity (the proportion of the total number of labeled ictal EEGs that are correctly classified), specificity (the proportion of the total number of labeled inter-ictal EEGs that are correctly classified), and classification accuracy (the proportion of the total number of EEG signals that are correctly classified) [2,[6][7][8][9][10][13][14][15][16][25][26][27][28][29][30][31][32]34,35,[38][39][40][41]49,53,59].…”

Section: Performance Evaluation-confusion Matrix Metricsmentioning

confidence: 99%

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Ren

Chai

et al. 2018

Applied Sciences

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This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).

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“…The independent components wavelet independent components (WICs) extracted during the processing of the dataset affected by eye blink artifacts. 4 …”

Section: Artifactual Epochs Rejectionmentioning

confidence: 99%

“…A new method for artifact removal from single-channel EEG recordings using nonnegative matrix factorization (NMF) in a Gaussian source separation framework was introduced by Damon et al [3], who focused their study only on ocular artifacts. A laudable comparison of different existing methods was made by Kirkove et al [4], who identified 11 existing methods for dealing with ocular artifacts. They have carried out a comparative performance evaluation of the resulting 27 distinct methods using a common set of data and a common set of metrics.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

Mammone

Morabito

2014

Entropy

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Electroencephalography (EEG) is a fundamental diagnostic instrument for many neurological disorders, and it is the main tool for the investigation of the cognitive or pathological activity of the brain through the bioelectromagnetic fields that it generates. The correct interpretation of the EEG is misleading, both for clinicians' visual evaluation and for automated procedures, because of artifacts. As a consequence, artifact rejection in EEG is a key preprocessing step, and the quest for reliable automatic processors has been quickly growing in the last few years. Recently, a promising automatic methodology, known as automatic wavelet-independent component analysis (AWICA), has been proposed. In this paper, a more efficient and sensitive version, called enhanced-AWICA (EAWICA), is proposed, and an extensive performance comparison is carried out by a step of tuning the different parameters that are involved in artifact detection. EAWICA is shown to minimize information loss and to outperform AWICA in artifact removal, both on simulated and real experimental EEG recordings.

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Comparative evaluation of existing and new methods for correcting ocular artifacts in electroencephalographic recordings

Cited by 26 publications

References 24 publications

A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain–Computer Interface Task Onset

A Novel Technique for Selecting EMG-Contaminated EEG Channels in Self-Paced Brain–Computer Interface Task Onset

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Enhanced Automatic Wavelet Independent Component Analysis for Electroencephalographic Artifact Removal

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