Artifact Removal in Electrocorticography Devices With Cardiac Contamination

Electrocorticography (ECoG) devices with electronics housed near the chest are susceptible to artifacts of a differing nature to electroencephalography (EEG) and standard ECoG. Using data obtained via an endovascular neural interface, we compared different artifact removal techniques in an offline setting with the aim of improving the quality and usefulness of clinically acquired data. Three different methods of filtration were applied and assessed: Common Average Referencing (CAR), Independent Component Analysis (ICA) with automated ECG channel selection, and Template-Based Removal (TBR). The automated ECG channel selection method was compared to manual selection. Methods were compared using signal-to-artifact root-mean-squared (RMS) values. The automated ECG source channel selection had high concordance with manual selection. All filtration methods decreased post-artifact RMS amplitudes and improved signal-to-artifact ratios. ICA took the most time to compute but had the most improved signal-to-artifact ratio. In regions with no ECG artifact, TBR preserved the underlying electrocorticography data better than the other methods. ICA with an automated method of ECG channel selection is the preferred method out of the three tested to remove ECG artifact while preserving the underlying signal. We establish methods that can be used to improve neural data of electrocorticography devices susceptible to cardiac contamination to facilitate translation as brain-computer interfaces.