De-noising epileptic EEG using ICA and phase synchrony.

A multi-channel recording of scalp electroencephalogram (EEG) is a non-invasive tool important for analysis and treatment of patients with epilepsy. These recordings are usually contaminated with artifacts and background activity, which may sometimes render them misleading or useless. Epileptic EEG is also useful for seizure detection, localisation and prediction. It would be useful to de-noise epileptic EEG in order to improve the efficiency of such diagnostic and prognostic procedures. The basic method of denoising a signal is through filtering, but filtering physiological signals is not trivial and highly subjective as the information is spread over different frequency bands and different measurement channels. This paper demonstrates a system for objectively de-noising epileptic EEG using Independent Component Analysis (ICA). In the standard implementation of ICA it is generally required to subjectively choose independent components (ICs) relevant to the epileptic activity; here we automate this process through the concept of phase synchronisation between ICs. In this manner de-noising the epileptic EEG with ICA becomes an objective (and automated) process.