Application Of Ica For Automatic Noise And Interference

Independent Component Analysis (ICA) and related methods like AdaptiveFactor Analysis (AFA) are promising novel approaches for elimination of artifacts and noise from biomedical 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 automatically by computer using criteria for classification, ordering and detection of noisy and random signals. The automatic detection and on-line elimination of noise and other interferences is especially important for long recordings, e.g. EEG/MEG recording during sleep. In this paper we focus mainly on the problem of `cleaning' or enhancementof noisy EEG/MEG data from noise and undesired interferences 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 randomness in independent components and classify independent signals). Preliminary extensive computer simulation confirmed potential usefulness of proposed methods for wide class of applications, especially in area of analysis and processing of EEG/MEG data