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In this study, movement-related potentials (MRPs) including Bereitschaftspotential (BP) is modeled by a dynamic Bayesian network model (DBNM). The containing the MRPs BP are divided into the early BP, NS' (negative slope) and MP (motor potential) intervals, each of which is represented by a Bayesian network model (BNM). Each BNM is constructed using the results by equivalent current dipole source localization (ECDL) after independent component analysis (ICA), for single-trial EEGs recorded during the hand movement. Nodes in the BNM correspond to the brain sites where dipoles are located. Connecting with the three BNMs yields a DBNM. This model is used to discriminate between the left- and right-hand movements in a framework of single-trial-electroencephalogram (EEG)-based BCI.

In magnetoencephalography (MEG) source localization, simplex method is widely used because it is simple and does not need the derivatives of the function. However, the simplex method is very sensitive to the choice of initial points. Thus, in this paper, a method of choosing the good initial starting points is proposed. First, the brain is divided into several partitions and the value of center of each partition is evaluated. Then, the point which has the lowest value is determined to be the initial point. In addition, we estimate the calculation time and the accuracy of the source localization according to increasing the number of partitions.

A wavelet network (WN) method is presented in this paper, which can be used to estimate the location and moment of an equivalent current dipole source using reduced-dimension data from the original measurement electroencephalography (EEG). In order to handle the large-scale high dimension problems efficiently and provide a real-time EEG dipole source localizer, the ISOMAP algorithm is firstly used to find the low dimensional manifolds from high dimensional EEG signal. Then, a WN is employed to discover the relationship between the observation potentials on the scalp and the internal sources within the brain. In our simulation experiments, satisfactory results are obtained.

Synchronization is a general phenomenon that appears in various fields of science (Andronov et al. 1966, Haken 1983, Blekhman 1988, Hayashi 1964, Landa 1996). As a consequence of its generality it lacks a unique definition (Blekhman 1988, Blekhman et al. 1995). In this chapter we understand synchronization in terms of phase synchronization (Rosenblum et al. 1996, Rosenblum et al. 1997a, Rosenblum et al. 1997b, Pikovsky et al. 1997a, Pikovsky et al. 1997b), i.e. adjustment of rhythms of two interacting self-sustained oscillators. This type of synchronization will be explained below. The concept of phase synchronization was already used for the analysis of bivariate data and revealed different synchronization patterns occuring in visually guided tracking movements (Tass et al. 1996) and in the cardiorespiratory interaction (Schafer et al. 1998).

Magnetic recordings will be used in this chapter for the extension of applications of concepts on dynamics of human potentials. In fact, the dynamics of potentials analyzed by means of evoked magnetic fields confirm and extend the results on EPs described in Chap. 9. Moreover, the evoked alpha oscillations can be elegantly and distinctly shown by this new type of measurement.

In the exciting area of biomagnetism, magnetoen-cephalography is one of the new diagnostic tools usable for epilepsies, but the method is still in technical and theoretical development and thus much more benefit must be expected.