Automatic removal of ocular artefacts using adaptive filtering and independent component analysis for electroencephalogram data

Abstract

A new method for eye movement artefacts removal based on independent component analysis (ICA) and recursive least squares (RLS) is presented. The proposed algorithm combines the effective ICA capacity of separating artefacts from brain waves, together with the online interference cancellation achieved by adaptive filtering. Eye blink, saccades, eyes opening and closing produce changes of potentials at frontal areas. For this reason, the method uses as a reference the electrodes closest to the eyes Fp1, Fp2, F7 and F8, which register vertical and horizontal eye movements in the electroencephalogram (EEG) caused by these activities as an alternative of using extra dedicated electrooculogram (EOG) electrodes, which could not always be available and could be subject to larger variability. Both reference signals and EEG components are first projected into ICA domain and then the interference is estimated using the RLS algorithm. The component related to EOG artefact is automatically eliminated using channel localisations. Results from experimental data demonstrate that this approach is suitable for eliminating artefacts caused by eye movements, and the principles of this method can be extended to certain other artefacts as well, whenever a correlated reference signal is available.