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).
Key Terms in this Chapter
Independent Component Analysis (ICA): A linear transformation for separating a multivariate signal into inner components, which are supposed to be as statistically independent as possible.
Independent Component (IC): Each of the inner components into which ICA decomposes a multivariate signal.
Artifact: Signal not produced by the brain, which appears in EEG or MEG recordings, and could bias the analyses of the neural activity. Some examples are the cardiac, ocular, muscular, and power line artifacts.
Higher-Order Statistics: Central moments of a statistical distribution with order higher than two.
Magnetoencephalogram (MEG): Neurophysiologic recording of the magnetic fields produced by the currents of the pyramidal neurons that flow parallel to the skull.
Blind Source Separation (BSS): The estimation of a set of unknown signals which have been mixed to produce a set of observed signals, with very little information about the former. Some common methods of BSS are ICA and PCA.
Electroencephalogram (EEG): Neurophysiologic recording of the voltage differences between different parts of the brain.
Principal Component Analysis (PCA): A linear transformation for separating a multivariate signal into inner components in such a way that the greatest variance by any projection of the data lies on the first component, the second greatest variance on the second coordinate, and so on.