The use of Ensemble Empirical Mode Decomposition with Canonical Correlation Analysis as a Novel Artifact Removal Technique

Biosignal measurement and processing is increasingly being deployed in ambulatory situations particularly in connected health applications. Such an environment dramatically increases the likelihood of artifacts which can occlude features of interest and reduce the quality of information available in the signal. If multichannel recordings are available for a given signal source then there are currently a considerable range of methods which can suppress or in some cases remove the distorting effect of such artifacts. There are however considerably fewer techniques available if only a single channel measurement is available and yet single channel measurements are important where minimal instrumentation complexity is required. This paper describes a novel artifact removal technique for use in such a context. The technique known as ensemble empirical mode decomposition with canonical correlation analysis (EEMD-CCA) is capable of operating on single channel measurements. The EEMD technique is first used to decompose the single channel signal into a multi-dimensional signal. The CCA technique is then employed to isolate the artifact components from the underlying signal using second order statistics. The new technique is tested against the currently available Wavelet denoising and EEMDICA techniques using both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data and is shown to produce significantly improved results