Artifact suppression from electroencephalography signals using stationary subspace analysis

Different types of artifacts contaminate the electroencephalography (EEG) signals in brain computer interface (BCI) application. Electrocardiography (ECG) is such potential artifact which negatively affects the BCI performance. This paper presents a novel method for ECG artifact elimination from EEG using stationary subspace analysis (SSA). It is based on the consideration that the ECG components are relatively non-stationary than that of the EEG signals. Applying SSA, the total channels of raw EEG are partitioned into two groups — stationary and non-stationary. The non-stationary channels contain the ECG artifacts. A statistical test is used to measure the degree of non-stationarity. The channel with highest non-stationarity is selected as the source of ECG artifact. The normalized ECG source is used to segregate the target artifact from the measured EEG. The result of the proposed method is compared with that of the well-known statistical method independent component analysis (ICA). The experimental evaluation illustrates that the proposed method is superior to the ICA based approach in terms of ECG artifact suppression from raw EEG.