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.
Highlights
Electroencephalography (EEG) and magnetoencephalography (MEG) are widely used non-invasive methods for measuring brain activity, the potential recorded at the scalp is frequently contaminated by additional external and physiological noise
In cued EEG datasets the percentage of hypothetical cardiac-related components (HCCs) classified as cardiac-related components (CCs) using only the Cardiac Identification Feature (CIF) feature was 22.7% for wet and 25.3% for dry datasets at 20 independent components (ICs) decomposition level, whereas it decreased to 16.7 and 24.0%, respectively, when using both features, demonstrating the higher selectivity of the combined use of CIF and CorrCI
In eyes-closed EEG datasets the percentage of HCCs classified as CCs using only the CIF feature was 29.7% for wet and 29.8% for dry datasets at 20 IC decomposition level, whereas it decreased to 8.1 and 19.1%, respectively, when using both features
Summary
Electroencephalography (EEG) and magnetoencephalography (MEG) are widely used non-invasive methods for measuring brain activity, the potential recorded at the scalp is frequently contaminated by additional external and physiological noise As this interference remains a pervasive problem in EEG and MEG research and clinical applications, there is a continuing need to develop effective techniques to minimize or eliminate artifacts (Urigüen and Garcia-Zapirain, 2015; Mannan et al, 2018). Cardiac-related artifacts possess highly stereotypic frequency and waveform characteristics, and methods developed to remove cardiac artifacts exploit these characteristics to identify cardiac interference and remove it from EEG and MEG data This is accomplished either by monitoring cardiac activity directly during data acquisition (i.e., through electrocardiography, ECG) and/or by exploring the temporal and spectral features of EEG data to detect potential artifactual elements (Al-Qazzaz et al, 2017; Radüntz et al, 2017; Waser et al, 2018). Many contemporary methods utilize a combination of techniques to improve cardiac artifact detection (Wang et al, 2013; Dora and Biswal, 2016; Kumar and Reddy, 2016; Patel et al, 2017; Dora and Biswal, 2019)
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