Automatic Removal of Cardiac Interference (ARCI): A New Approach for EEG Data

Abstract

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.

Is Brain Dynamics Preserved in the EEG After Automated Artifact Removal? A Validation of the Fingerprint Method and the Automatic Removal of Cardiac Interference Approach Based on Microstate Analysis

“…In the automated classification of artifactual ICs, we applied the fingerprint method optimized for detecting and removing ICs containing eyeblinks, eye movements, and myogenic artifacts ( Stone et al, 2018 ), whereas the ARCI approach ( Tamburro et al, 2019 ) was used to classify the ICs containing cardiac-related artifacts, including pulse interference.…”

“…The ARCI approach evaluates time and frequency features of the separated ICs to identify ICs that contain electrical cardiac artifacts and/or interference due to the pulsatile activity of the heart ( Tamburro et al, 2019 ). The ARCI approach was applied to the same sets of ICs to classify ICs containing electrical cardiac and cardiovascular artifacts.…”

“…The original fingerprint method was subsequently optimized to obtain a more efficient and reliable classification of ICs containing eyeblinks, eye movements, and myogenic interference by means of smaller number of features ( Stone et al, 2018 ). Given the poor performance of the fingerprint method in identifying ICs containing cardiac interference, we developed another method, the automatic removal of cardiac interference (ARCI) approach ( Tamburro et al, 2019 ), where new features were introduced to specifically detect and remove the ICs containing cardiac-related artifacts, including pulse-related interferences.…”

Special Report on the Impact of the COVID-19 Pandemic on Clinical EEG and Research and Consensus Recommendations for the Safe Use of EEG

“…Some of these methods recently succeeded to combine all desirable properties: automatic detection of artefacts, good performance, good generalizability, efficiency, and transparency. 175,176 Online versions of these methods are presently under development; they will be extremely beneficial for clinical applications where time is crucial for a quick diagnosis.…”

EEG-Beats: Automated analysis of heart rate variability (HVR) from EEG-EKG

“…Traditionally, the focus of EKG in EEG experiments has been the removal of cardiac interference (Tamburro et al, 2019). Our interest in HRV arose from the possibility of using HRV indicators secondary measures of subject physiological state during EEG experiments.…”