Removing electroencephalographic artifacts by blind source separation.

  title={Removing electroencephalographic artifacts by blind source separation.},
  author={Tzyy-Ping Jung and Scott Makeig and Colin J. Humphries and T. W. Lee and Martin J. McKeown and Vicente J. Iragui and Terrence J. Sejnowski},
  volume={37 2},
Eye movements, eye blinks, cardiac signals, muscle noise, and line noise present serious problems for electroencephalographic (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… 

Rejection of Electro-oculograpic Artifacts from EEG Data Using ICA Algorithm

The results on EEG data of normal and autistic subjects show that ICA can effectively detect, separate, and remove contamination from a wide variety of artifactual sources especially due to EOG in EEG records.

Removing Electroencephalographic Artifacts by Independent Components Analysis

The independent components analysis (ICA) method is used to separate the clean data from the rest of the sources, artifacts within the brain and clear the indpendent components is not brain activity sources and get EEG signals without the artifacts.

Combining EEG and eye tracking: identification, characterization, and correction of eye movement artifacts in electroencephalographic data

An algorithm, which uses eye tracker information to objectively identify eye-artifact related ICA-components (ICs) in an automated manner is proposed, which performed very similar to human experts when those were given both, the topographies of the ICs and their respective activations in a large amount of trials.

Correction of Ocular Artifacts in EEG Recordings using Empirical Mode Decomposition

This paper explores the use of empirical mode decomposition (EMD) based filtering technique to correct the eye blinks and eye movement artifacts in single channel EEG data.

Eye-blink artifact removal from single channel EEG with k-means and SSA

A new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal is developed.

Common Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach

A common methodology to suppress both cardiac and ocular artifact signal, by correlating the measured contaminated EEG signals with the clean reference electro-oculography (EOG) and electrocardiography (EKG) data and subtracting the scaled EOG and EKG from the contaminated EEG recording.

Hybrid EEG—Eye Tracker: Automatic Identification and Removal of Eye Movement and Blink Artifacts from Electroencephalographic Signal

An automatic framework based on independent component analysis (ICA) and system identification to identify and remove ocular artifacts from EEG data by using hybrid EEG and eye tracker system is proposed.

An iterative subspace denoising algorithm for removing electroencephalogram ocular artifacts

SSA with CWT and k-Means for Eye-Blink Artifact Removal from Single-Channel EEG Signals

A new methodology is proposed by integrating the SSA with continuous wavelet transform (CWT) and the k-means clustering algorithm that removes the eye-blink artifact from the single-channel EEG signals without altering the low frequencies of the EEG signal.



Independent Component Analysis of Electroencephalographic Data

First results of applying the ICA algorithm to EEG and event-related potential (ERP) data collected during a sustained auditory detection task show that ICA training is insensitive to different random seeds and ICA may be used to segregate obvious artifactual EEG components from other sources.

A spectral method for removing eye movement artifacts from the EEG.

Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition.

A variation of this technique in which the factors that reconstruct the modified EEG from the original are stored as a matrix is developed, which acts as a spatial filter with useful properties and successfully applied this method to remove artifacts, including ocular movement and electrocardiographic artifacts.

Removal of the ocular artifact from the EEG: a comparison of time and frequency domain methods with simulated and real data.

Estimated transfer parameters suggested that these data were marked by weak frequency dependence only, which can be accounted for by simple time domain regression (and also by the other two methods).

Analyzing and Visualizing Single-Trial Event-Related Potentials

It is shown that sorting single-trial ERP epochs in order of reaction time and plotting the potentials in 2-D clearly reveals underlying patterns of response variability linked to performance, and a new visualization tool, the 'ERP image', is proposed for investigating variability in latencies and amplitudes of event-evoked responses in spontaneous EEG or MEG records.

Eye movement artifact in the CNV.

Blind Separation of Event-Related Brain Responses into Independent Components

Abstract : Functional imaging of brain activity based on changes in blood flow does not supply information about the relative timing of brief bursts of neural activity in different brain areas.

Independent Component Analysis of Simulated ERP Data

The ability of the ICA algorithm to decompose brief event-related potential (ERP) data sets into temporally independent components by applying it to simulated ERP-length EEG data synthesized from 3-sec (600-point) electrocorticographic (ECoG) epochs recorded from the cortical surface of a human undergoing pre-surgical evaluation is demonstrated.

Correction of EOG artifacts in event-related potentials of the EEG: aspects of reliability and validity.

Eye artifact correction based on the trimmed group means of these rates is superior to the conventional rejection in terms of reducing correlation between EOG and EEG.

Dipole models of eye movements and blinks.

  • P. BergM. Scherg
  • Medicine
    Electroencephalography and clinical neurophysiology
  • 1991