APPLICATION OF ICA FOR AUTOMATIC NOISE AND INTERFERENCE CANCELLATION IN MULTISENSORY BIOMEDICAL SIGNALS

Andrzej Cichocki and Sergiy Vorobyov
E­mail: cia(svor)@brain.riken.go.jp

Independent Component Analysis (ICA) and related meth­ ods like Adaptive Factor Analysis (AFA) are promising novel approaches for elimination of artifacts and noise from bio­ medical signals, especially EEG/MEG data. However, most of the methods require manual detection and classification of interference components. Main objective of this paper is to detect and eliminate noise and some artifacts automat­ ically by computer using criteria for classification, order­ ing and detection of noisy and random signals. The auto­ matic detection and on­line elimination of noise and other interferences is especially important for long recordings, e.g. EEG/MEG recording during sleep. In this paper we fo­ cus mainly on the problem of `cleaning' or enhancement of noisy EEG/MEG data from noise and undesired interfer­ ences using several techniques: ICA and HOS measure of Gaussianity (to detect and eliminate Gaussian noise), linear predictor (to detect i.i.d. sources and classify temporally structured sources) and Hurst exponent (to detect random­ ness in independent components and classify independent signals). Preliminary extensive computer simulation con­ firmed potential usefulness of proposed methods for wide class of applications, especially in area of analysis and pro­ cessing of EEG/MEG data.