Review of State of Art in Electrooculogram Artifact Removal from Electroencephalogram Signals (original) (raw)
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Removal of ocular artifacts from EEG signals: a comparison of techniques
Many artifact rejection techniques have been proposed for removing electrooculographic (EOG) artifacts from electroencephalographic (EEG) signal. To date, no method has established itself as a golden standard. In this study, we compare the performance of three artifact removal techniques: a fully automated regression filtering method based on Least Mean Square (LMS) adaptive filter algorithm, and two methods based on blind source separation (BSS) techniques. The first BSS method uses the Extended-Independent Component Analysis (ext-ICA) to separate the signal into sources. The second BSS method uses Second Order Blind Identification (SOBI) for source separation and Fractal Dimension (FD) for the automatic identification of EOG artifacts. Each algorithm was applied on one hundred multichannel EEG epochs contaminated with eye blinks. The performance of the algorithms was defined as the difference between the correlation of EOG and the contaminated signal and the correlation of EOG with the clean signal. One -way ANOVA revealed statistical significant differences among the performances of the three algorithms (F(2.297)=3.291 p-value = 0.039). The LMS approach presented the best performance (0.23 ±0.11) in contrast to other two algorithms ext -ICA (0.20±0.11) and FD (0.19±0.11).
A Review on EEG Artifacts and its Different Removal Technique
International Journal of Signal Processing, Image Processing and Pattern Recognition, 2016
Electroencephalograms are the neurological signals which help in the study of various diseases. These are often contaminated with various artifacts. It is difficult to study and analysis of brain signals in the existence of these artifacts. EOG, ECG, motion and EMG are the common artifacts which cause disturbance to neurological signal. This review paper focuses on the artifact removal techniques with their features. Important parameters were taken into consideration while the study of various published papers. Strength and weakness of each paper are mentioned. This review of various papers is best of my knowledge.
EEG Artifacts Removal: A Detailed Review Based Study
International Journal For Multidisciplinary Research
Electroencephalography, or EEG for short, is a technique used to record the electrical activity of the brain. This EEG detects errors that affect how the human brain functions. This method is the most commonly used for recording the brain in laboratory research, clinical investigations, patient health monitoring, diagnostics, and a variety of other applications due to its non-invasiveness and cost-benefit ratio. Most EEG recordings are contaminated by a variety of irregularities, including those caused by electrode displacement, motion, ocular, and muscular activity related EMG anomalies. These unwanted artifacts may make it difficult to distinguish genuine information from them, in addition to confusing the brain's information processing that supports them. EEG signal artifacts can be removed in a variety of ways. The top and most popular artifact reduction techniques are listed on this page as PCA, pure EEG, and wavelet transform. The study provides a thorough evaluation of cu...
EEG Artifact Removal Techniques: A Comparative Study
2020
An Electroencephalograph (EEG) is widely used to study the working of brain. The main challenge in dealing with such signals is its non-stationary nature and high dimensionality. The propagation of signals from one electrode to other gives rise to presence of artifacts in the data. This can lead to generation of false results while working on the data. Ocular, Muscular, and Cardiac are some of the very common artifacts for a dataset dedicated for motor imagery classification. This paper deals with the techniques and their comparative analysis for removal of ocular artifact from EEG signals. On the basis of dataset, the paper shows a study of how Linear Regression, Filtering, and Independent Component Analysis(ICA) works on the removal of ocular artifact in this data. To verify the effect of artifact removal technique features were extracted from the data, before and after applying different techniques and further fed to the classifier. An improvement in the accuracy of motor imagery...
Removal of EOG Artefact from EEG – A Review
IOS Press eBooks, 2022
An electroencephalogram (EEG) is a medical examination that records the electrical activity of the human brain. In order to record these signals, electrodes are placed on the scalp, and these electrodes detect any activity of the brain cells. This result is used to diagnose patients with epilepsy, sleep disorders, brain tumors, seizure disorders, and brain disorders. However, these signals are often corrupted by various artefacts either generated from the human body(physiological) or the electrical apparatus used(non-physiological). The removal of these artefacts is vital as their presence may result in the wrong diagnosis of a disease. One of these artefacts is generated because of various kinds of eye movements, i.e. EOG. In this paper, we will be focusing on how ocular artefacts occur and how they affect the EEG signal. Later we will look into different types of artefact removal techniques and how they perform differently from each other on the basis of different factors such as signal-to-noise-ratio etc. The aim of this study is to get a detailed understanding of various ocular artefact removal techniques, challenges related to them and finding an efficient way to obtain an uncorrupted EEG signal.
A REVIEW ON "OCULAR ARTIFACTS IN EEG AND THEIR REMOVAL TECHNIQUES"
Journal of Research in Engineering and Applied Sciences, 2017
This paper presents an extensive review on understanding all types of EEG artifacts which are incorporated in EEG signals while taking measurements from scalp of the different subjects. Artifacts which are more prominent and occurred very often are 'Ocular artifacts'. The characteristics of an EEG signal having OA's are mentioned and explained in the paper. Ocular artifacts have a large impact on measurement. Reasons for inclusion of these artifacts and why the elimination is essential have been explained in this paper. Artifacts can be also named as noise; and noise has to be removed from the signal. For removing the OA's signal modeling is essential. Many Artifact correction approaches have been developed to provide reliable results of identification detection and removal. The different methods like ICA, WT, ANN etc and their characteristics which are explained by the researchers is the main review part of this paper which will give us an idea of building a more accurate system algorithm to remove ocular artifacts from the contaminated EEG signal. In This paper, the different methods and their result parameters along with their values have been given in the tabular form.
Signal Processing, 2014
EEG signals are often contaminated by ocular artifacts (OAs), in particular when they are recorded for a subject that is, in principle, awake, such as in a study of drowsiness. It is generally desirable to detect and/or correct these OAs before interpreting the EEG signals. We have identified 11 existing methods for dealing with OAs. Their study allowed us to create 16 new methods. We performed a comparative performance evaluation of the resulting 27 distinct methods using a common set of data and a common set of metrics. The data was recorded during a driving task of about two hours in a driving simulator. This led to a ranking of all methods, with five emerging clear winners, comprising two existing methods and three new ones.
EOG Artifact Correction from EEG Signals for Biomedical Analysis
International Journal of Computer Applications, 2012
The electroencephalogram records the electrical activity of the brain and is the main resource of information for studying neurological disorders. Corruption of EEG signal is caused by occurrence of various artifacts like line interference, electrooculogram, electrocardiogram, and muscle activity. These artifacts increase the difficulty in analyzing the EEG and obtaining clinical information. The ocular artifact detection and correction from EEG is of considerable significance for both the automatic and visual analysis of brainwave activity by neurologists for proper diagnosis. In this paper, a statistical method for removing ocular artifacts from EEG recordings through thresholding and correlation is proposed. EEG database of 325 samples from Colorado state university is used for experimentation. The mean, variance, standard deviation, and correlation are the performance metrics used. The results show that the proposed method significantly detects and removes the EOG and line frequency artifact without loss of important part of original EEG.
The presence of ocular artifacts (OA) due to eye movements and eye blinks is a major problem for the analysis of electroencephalographic (EEG) recordings in most applications. A large variety of methods (algorithms) exist for detecting or/and correcting OA's. We identified the most promising methods, implemented them, and compared their performance for correctly detecting the presence of OA's. These methods are based on signal processing "tools" that can be classified into three categories: wavelet transform, adaptive filtering, and blind source separation. We evaluated the methods using EEG signals recorded from three healthy persons subjected to a driving task in a driving simulator. We performed a thorough comparison of the methods in terms of the usual performances measures (sensitivity, specificity, and ROC curves), using our own manual scoring of the recordings as ground truth. Our results show that methods based on adaptive filtering such as LMS and RLS appear to be the best to successfully identify OA's in EEG recordings.
Removal of the ocular artifacts from EEG data using a cascaded spatio-temporal processing
Computer Methods and Programs in Biomedicine, 2006
c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 8 3 ( 2 0 0 6 ) 95-103 Electroencephalogram (EEG) Artifact electrooculogram Principal components analysis (PCA) Minimum norm estimation a b s t r a c t Eye movements and blinks may produce unusual voltage changes in human electroencephalogram (EEG). These effects may spread across scalp and mask brain signals. In this paper, a cascaded spatio-temporal processing procedure (CAST) is presented to remove artifact electrooculogram (EOG). Firstly a discrete equivalent distributed source on the cortical surface is reconstructed from the contaminated scalp recordings by a linear minimum norm estimation (i.e. a spatial analysis step). Then, the equivalent sources of EOG are identified by principal component analysis (PCA) of the equivalent distributed source time series (i.e.