Enhancing the signal-to-noise ratio of ICA-based extracted ERPs

Steven Lemm, Gabriel Curio, Yevhen Hlushchuk, Klaus Robert Müller

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings.

Original languageEnglish
Article number1608509
Pages (from-to)601-607
Number of pages7
JournalIEEE Transactions on Biomedical Engineering
Volume53
Issue number4
DOIs
Publication statusPublished - 2006 Apr 1

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Keywords

  • Bioelectrical potentials
  • Electroencephalogram (EEG)
  • Independent component analysis (ICA)
  • Signal-to-noise ratio

ASJC Scopus subject areas

  • Biomedical Engineering

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