TY - JOUR
T1 - Get rid of the beat in mobile EEG applications
T2 - A framework towards automated cardiogenic artifact detection and removal in single-channel EEG
AU - Chiu, Neng Tai
AU - Huwiler, Stephanie
AU - Ferster, M. Laura
AU - Karlen, Walter
AU - Wu, Hau Tieng
AU - Lustenberger, Caroline
N1 - Publisher Copyright:
© 2021 The Author(s)
PY - 2022/2
Y1 - 2022/2
N2 - 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.
AB - 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.
KW - Automated artifact removal
KW - Cardiogenic artifact
KW - Electroencephalogram
KW - Machine learning
KW - Mobile technology
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U2 - 10.1016/j.bspc.2021.103220
DO - 10.1016/j.bspc.2021.103220
M3 - Article
AN - SCOPUS:85118473745
SN - 1746-8094
VL - 72
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 103220
ER -