Sparsity-based correction of exponential artifacts

Yin Ding, Ivan Selesnick

Research output: Contribution to journalArticle

Abstract

This paper describes an exponential transient excision algorithm (ETEA). In biomedical time series analysis, e.g., in vivo neural recording and electrocorticography (ECoG), some measurement artifacts take the form of piecewise exponential transients. The proposed method is formulated as an unconstrained convex optimization problem, regularized by smoothed ℓ1-norm penalty function, which can be solved by majorization-minimization (MM) method. With a slight modification of the regularizer, ETEA can also suppress more irregular piecewise smooth artifacts, especially, ocular artifacts (OA) in electroencephalography (EEG) data. Examples of synthetic signal, EEG data, and ECoG data are presented to illustrate the proposed algorithms.

Original languageEnglish (US)
Pages (from-to)236-248
Number of pages13
JournalSignal Processing
Volume120
DOIs
StatePublished - Mar 1 2016

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Electroencephalography
Time series analysis
Convex optimization

Keywords

  • Artifact removal
  • Signal decomposition
  • Sparsity

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition

Cite this

Sparsity-based correction of exponential artifacts. / Ding, Yin; Selesnick, Ivan.

In: Signal Processing, Vol. 120, 01.03.2016, p. 236-248.

Research output: Contribution to journalArticle

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