Ongoing EEG artifact correction using blind source separation

…, 2000

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroenceph-alographic~EEG! interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic~EOG! recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis~PCA! has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis~ICA!. Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

International Journal for Research in Applied Science and Engineering Technology, 2017

In recent researches, Electroencephalography (EEG) gains a widespread popularity. There is maximum probability of artifact with EEG signal because of physical and experimental problems therefore artifact elimination is a central issue during encephalogram recordings. Although many researchers have doing research in this area and developed their own method for artifact elimination like independent component analysis (ICA), average artifact subtraction (AAS), real time independent component analysis (ICA), Recursive Least Squares (RLS) adaptive filter, Spatially Constrained Independent Component Analysis(SCICA), Blind Source Separation and Wavelet Denoising, still visual examination by experts is needed. Finding the artifacts and eliminating them from real EEG signal by the use of competent algorithm assists researchers and doctors. This paper discusses the various methods along with limitations of automatic EEG artifact removal techniques.

Clinical EEG and neuroscience : official journal of the EEG and Clinical Neuroscience Society (ENCS), 2013

Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is an increased need for automated artifact removal methods. However such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2006

The EEG signal is a record of the brain activity using multiple electrodes placed on the scalp. Unfortunately, it can be hardly contaminated by a lot of noises called artifacts. These latter can be generated by various actions such as eye blinks, eye movements or the skeletal muscle activities (jaw, forehead, ...). This study will focus on a global artifact removal method using independent component analysis (ICA) on signals cut in frequency bands. The interest of this method resides in automatizing the artifactual source identification and enables a global filtering of records using constant bases. A brief overview of the project will be made in order to introduce the method used. Next, the results will be presented and their validation will be discussed in the conclusion.

High amplitude artifacts represent a problem during EEG recordings in neuroscience research. Taking this into account , this paper proposes a method to identify high amplitude artifacts with no requirement for visual inspection, electrooculo-gram (EOG) reference channel or user assigned parameters. A potential solution to the high amplitude artifacts (HAA) elimination is presented based on the blind source separation technique. The assumption underlying the selection of components is that HAA are independent of the EEG signal and different HAA can be generated during the EEG recordings. Therefore, the number of components related to HAA is variable and depends on the processed signal, which means that the method is adaptable to the input signal. The results demonstrate that the proposed method preferably removes the signal associated to the delta band and maintains the EEG signal information in other bands with a high relative precision, thus improving the quality of the EEG signal....

IFMBE Proceedings, 2010

Electroencephalography (EEG) is one of the most effective diagnostic procedure for epilepsy. However , the presence artifacts like electro-oculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and powerline noise 50hz in the EEG signal is a major problem in the study of brain potentials. A variety of algorithms have been proposed to reject these artifacts and noise including methods based on regression and blind source separation (BSS) techniques. In this study, the performances of two widely used artifact rejection techniques are presented.One based on Least Mean Square Adaptive noise cancellation (ANC) for removing ECG artifact and powerline noise. And the another is BSS technique which uses the Second Order Blind Identification (SOBI) to reject EMG and EOG artifacts. Each algorithm was applied in real EEG data and then their performance quantified in the time domain.

2010

Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroenceph-alographic~EEG! interpretation and analysis when rejecting contaminated EEG segments results in an unacceptable data loss. Many methods have been proposed to remove artifacts from EEG recordings, especially those arising from eye movements and blinks. Often regression in the time or frequency domain is performed on parallel EEG and electrooculographic~EOG! recordings to derive parameters characterizing the appearance and spread of EOG artifacts in the EEG channels. Because EEG and ocular activity mix bidirectionally, regressing out eye artifacts inevitably involves subtracting relevant EEG signals from each record as well. Regression methods become even more problematic when a good regressing channel is not available for each artifact source, as in the case of muscle artifacts. Use of principal component analysis~PCA! has been proposed to remove eye artifacts from multichannel EEG. However, PCA cannot completely separate eye artifacts from brain signals, especially when they have comparable amplitudes. Here, we propose a new and generally applicable method for removing a wide variety of artifacts from EEG records based on blind source separation by independent component analysis~ICA!. Our results on EEG data collected from normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably with those obtained using regression and PCA methods. ICA can also be used to analyze blink-related brain activity.

2006

The procedure is based on blind source separation (BSS) and, in contrast to methods already available in the literature, it is completely automated and does not require the availability of peri-ocular EOG electrodes. The proposed approach removed most EOG artifacts in 6 longterm EEG recordings containing epilectic seizures without distorting the recorded ictal activity.

Wireless Personal Communications, 2019

As the electroencephalography (EEG) biomedical signals are affected under the presence of the muscular motion artifacts. Presence of these artifacts leads to error in visual analysis of EEG signal, thus results in wrong diagnosis of human diseases. The variants of blind source separation (BSS) methods are available. This paper aims to design the efficient BSS based method for effectively eradicating the EEG motion artifacts. This is accomplished by evaluating the six different methods, which are combination of independent component analysis (ICA) and canonical correlation analysis (CCA) along with the discrete wavelet transform and stationary wavelet transform methods. Each of above combination methods are applied on the ensemble empirical mode decomposed, Intrinsic Mode Functions for EEG motion artifact suppression. This research paper tests the performance over pure EEG signal and also on the simulated EEG sinusoids to mimic the effect of motion artifacts. The performance of six BSS artifact removal algorithms are evaluated using efficiency matrices such as del signal to noise ratio, lambda (λ), spectral distortion (P dis) and root mean square error. The execution time is also calculated to evaluate the computation efficiency of the algorithms. The results suggest that CCA algorithm outperforms over ICA in the case of the high noisy condition of EEG signal.

Neural Networks for …, 1998

Pervasive electroencephalographic (EEG) artifacts associated with blinks, eye-movements, muscle noise, cardiac signals, and line noise poses a major challenge for EEG interpretation and analysis. Here, we propose a generally applicable method for removing a wide variety of artifacts from EEG records based on an extended version of an Independent Component Analysis (ICA) algorithm 2, 12] for performing blind source separation on linear mixtures of independent source signals. Our results show that ICA can e ectively separate and remove contamination from a wide variety of artifactual sources in EEG records with results comparing favorably to those obtained using Principal Component Analysis.