Real-Time Cardiac Artifact Removal from EEG Using a Hybrid Approach

Abstract

BACKGROUND: Electroencephalogram (EEG) signals are sometimes contaminated by cardiac artifacts (CAs). The artifacts resulted by electrical activities of heart, named electrocardiogram (ECG), appear in EEG recordings as spiky potentials that may obscure the information in EEG data and reduce their interpretability. OBJECTIVE: Real-time removal of CAs is of great importance in several applications of EEG, and particularly brain-computer interface (BCI). The process is, however, often neglected due to the time-consuming computations. METHODS: This paper applies a new real-time hybrid approach to remove ECG artifacts from EEG signals. The method is based on the combination of independent component analysis (ICA) and adaptive noise cancellation (ANC), referred to as ICA-ANC. ICA is applied to a few EEG signals in order to extract the reference signal for ANC. The method so utilizes a few EEG channels without synchronous ECG channel, and thus is suited to portable BCI applications. RESULTS: ICA-ANC is evaluated for datasets of five different subjects. CAs are efficiently removed while preserving the cerebral information. The approach is shown to outperform a state of the art method. CONCLUSION: The proposed new algorithm is capable of real-time cardiac artifacts removal using a few EEG channels.