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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Neurosci. | doi: 10.3389/fnins.2019.00441

Automatic Removal of Cardiac Interference (ARCI): a new approach for EEG data

  • 1Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti–Pescara, Italy
  • 2Department of Neuroscience, Imaging and Clinical Sciences, University of "G. d'Annunzio" Chieti - Pescara, Italy

EEG recordings are generally affected by interference from physiological and non physiological sources which may obscure underlying brain activity and hinder effective EEG analysis. In particular, cardiac interference can be caused by the electrical activity of the heart and/or cardiovascular activity related to blood flow. Successful EEG application in sports science settings requires a method for artifact removal that is automatic and flexible enough to be applied in a variety of acquisition conditions without requiring simultaneous ECG recordings that could restrict movement.

We developed an automatic method for classifying and removing both electrical cardiac and cardiovascular artifacts (ARCI) that does not require additional ECG recording. Our method employs independent component analysis (ICA) to isolate data independent components (ICs) and identifies the artifactual ICs by evaluating specific IC features in the time and frequency domains. We applied ARCI to EEG datasets with cued artifacts and acquired during an eyes-closed condition. Data were recorded using a standard EEG wet cap with either 128 or 64 electrodes and using a novel dry electrode cap with either 97 or 64 dry electrodes. All data were decomposed into different numbers of components to evaluate the effect of ICA decomposition level on effective cardiac artifact detection. ARCI performance was evaluated by comparing automatic ICs classifications with classifications performed by experienced investigators. Automatic and investigator classifications were highly consistent resulting in an overall accuracy greater than 99% in all datasets and decomposition levels, and an average sensitivity greater than 90%. Best results were attained when data were decomposed into a fewer number of components where the method achieved perfect sensitivity (100%). Performance was also evaluated by comparing automatic component classification with externally recorded ECG. Results showed that ICs automatically classified as artifactual were significantly correlated with ECG activity whereas the other ICs were not. We also assessed that the interference affecting EEG signals was reduced by more than 82% after automatic artifact removal.

Overall, ARCI represents a significant step in the detection and removal of cardiac-related EEG artifacts and can be applied in a variety of acquisition settings making it ideal for sports science applications.

Keywords: automatic artifact removal, Cardiovascular artifacts, EEG, ICA, Cardiac artifacts

Received: 06 Aug 2018; Accepted: 17 Apr 2019.

Edited by:

Paolo Gargiulo, Landspitali University Hospital -LSH

Reviewed by:

Ceon Ramon, University of Washington, United States
Mahmoud Hassan, University of Rennes 1, France
Kyle J. Edmunds, University of Oxford, United Kingdom  

Copyright: © 2019 Tamburro, Stone and Comani. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Silvia Comani, Behavioral Imaging and Neural Dynamics Center, G. d'Annunzio University of Chieti–Pescara, Chieti, Italy,