Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG

Neng Tai Chiu, Stephanie Huwiler, M. Laura Ferster, Walter Karlen, Hau Tieng Wu, Caroline Lustenberger

Research output: Contribution to journalArticlepeer-review

Abstract

Brain activity recordings outside clinical or laboratory settings using mobile EEG systems have gained popular interest allowing for realistic long-term monitoring and eventually leading to identification of possible biomarkers for diseases. The less obtrusive, minimized systems (e.g., single-channel EEG, no ECG reference) have the drawback of artifact contamination with varying intensity that are particularly difficult to identify and remove. We developed brMEGA, the first open-source algorithm for automated detection and removal of cardiogenic artifacts using non-linear time-frequency analysis and machine learning to (1) detect whether and where cardiogenic artifacts exist, and (2) remove those artifacts. We compare our algorithm against visual artifact identification and a previously established approach and validate it in one real and semi-real datasets. We demonstrated that brMEGA successfully identifies and substantially removes cardiogenic artifacts in single-channel EEG recordings. Moreover, recovery of cardiogenic artifacts, if present, gives the opportunity for future extraction of heart rate features without ECG measurement.

Original languageEnglish (US)
Article number103220
JournalBiomedical Signal Processing and Control
Volume72
DOIs
StatePublished - Feb 2022

Keywords

  • Automated artifact removal
  • Cardiogenic artifact
  • Electroencephalogram
  • Machine learning
  • Mobile technology

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics
  • Biomedical Engineering

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