Removing Electroencephalographic Artifacts by Blind Source Separation

Abstract

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 ~EEG! recordings to derive parameters characterizing the appearance and spread of EEG 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.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2000
Accession Number
ADA455940

Entities

People

  • Colin Humphries
  • Marin J. Mckeown
  • Scott Maeig
  • Te-won Lee
  • Terrence J. Sejnowski
  • Tzyy-Ping Jung
  • Vicente Iragui

Organizations

  • Naval Health Research Center

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Data Science
  • Electrical Engineering
  • Eye
  • Eye Movements
  • Factor Analysis
  • Frequency
  • Frequency Domain
  • Head (Anatomy)
  • Information Processing
  • Information Science
  • Information Systems
  • Neural Networks
  • Neurology
  • Regression Analysis
  • Signal Processing
  • Spatial Distribution
  • Statistical Analysis

Fields of Study

  • Computer science

Readers

  • Brain and Cognitive Science; Experimental Psychology; Cognitive Neuroscience
  • Regression Analysis.