Get rid of the beat in mobile EEG applications: A framework towards automated cardiogenic artifact detection and removal in single-channel EEG
OPEN ACCESS
Loading...
Author / Producer
Date
2022-02
Publication Type
Journal Article
ETH Bibliography
yes
Citations
Altmetric
OPEN ACCESS
Data
Rights / License
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.
Permanent link
Publication status
published
External links
Editor
Book title
Journal / series
Volume
72
Pages / Article No.
103220
Publisher
Elsevier
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Electroencephalogram; Cardiogenic artifact; Automated artifact removal; Mobile technology; Machine learning
Organisational unit
09533 - Karlen, Walter (ehemalig) / Karlen, Walter (former)