Skip to content
Conference proceedings

Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA

Weidong Zhou, Gotman J ...see all

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3 pp. 392-395 Published by IEEE

  • 64

    Readers

    Mendeley users who have this article in their library.
  • 31

    Citations

    Citations of this article.
  • N/A

    Views

    ScienceDirect users who have downloaded this article.
Sign in to save reference

Abstract

In this study, the methods of wavelet threshold de-noising and independent component analysis (ICA) are introduced. ICA is a novel signal processing technique based on high order statistics, and is used to separate independent components from measurements. The extended ICA algorithm does not need to calculate the higher order statistics, converges fast, and can be used to separate subGaussian and superGaussian sources. A pre-whitening procedure is performed to de-correlate the mixed signals before extracting sources. The experimental results indicate the electromyogram (EMG) and electrocardiograph (ECG) artifacts in electroencephalograph (EEG) can be removed by a combination of wavelet threshold de-noising and ICA.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Weidong Zhou

  • J. Gotman

Cite this document

Choose a citation style from the tabs below