A linear transformation for separating a multivariate signal into inner components, which are supposed to be as statistically independent as possible.
Published in Chapter:
Applying Independent Component Analysis to the Artifact Detection Problem in Magnetoencephalogram Background Recordings
Javier Escudero (University of Valladolid, Spain), Roberto Hornero (University of Valladolid, Spain), Daniel Abásolo (University of Valladolid, Spain), Jesús Poza (University of Valladolid, Spain), and Alberto Fernández (University of Valladolid, Spain)
Copyright: © 2008
|Pages: 9
DOI: 10.4018/978-1-59904-889-5.ch012
Abstract
The analysis of the electromagnetic brain activity can provide important information to help in the diagnosis of several mental diseases. Both electroencephalogram (EEG) and magnetoencephalogram (MEG) record the neural activity with high temporal resolution (Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993). Nevertheless, MEG offers some advantages over EEG. For example, in contrast to EEG, MEG does not depend on any reference point. Moreover, the magnetic fields are less distorted than the electric ones by the skull and the scalp (Hämäläinen et al., 1993). Despite these advantages, the use of MEG data involves some problems. One of the most important difficulties is that MEG recordings may be severely contaminated by additive external noise due to the intrinsic weakness of the brain magnetic fields. Hence, MEG must be recorded in magnetically shielded rooms with low-noise SQUID (Superconducting QUantum Interference Devices) gradiometers (Hämäläinen et al., 1993).