Subspace denoising of EEG artefacts via multivariate EMD

The components obtained using the time-frequency algorithm empirical mode decomposition (EMD) enable unique advantages in the context of noise removal. In this paper, recent EMD-based de-noising methods are reviewed and similarities with a more conventional class of techniques - subspace denoising - are illustrated. Standard subspace approaches which are based on the factorisation of covariance matrices are unsuitable for nonstationary data. By comparison, EMD facilitates a signal representation which enables denoising using short spatio/temporal windows. It is highlighted how the EMD property of local orthogonality can be extended via multivariate operations, and a denoising scheme is proposed and compared with standard methods in electroencephalogram (EEG) artefact-removal using a novel multimodal sensor.