**4. Clinical applications**

522 Biomedical Science, Engineering and Technology

without the need to distinguish between noise and chaos (Gans et al., 2009). An example of bivariate synchronization between two EEG sensors in the right medical parietal region is shown in figure 2. The synchronization likelihood in this example was computed using the only the first three IMFs from each sensor, without searching for cross band

To identify synchronized clusters of EEG channels, a method based on an eigenvector space method, using eigenvalues of R, can be used, following the algorithm developed and applied in (Allefeld and Bialonski, 2007; Bialonski and Lehnertz, 2006; Fine et al., 2010). The outcome of this algorithm will be synchronized clusters of EEG channels. These may be mapped onto scalp plots and the identified clusters compared to default mode networks that have been identified in young children (Sauseng and Klimesch, 2008; Supekar et al., 2009; Supekar et al., 2010). It will be of particular interest to determine if synchronization clusters are significantly correlated with functional networks in the brain and are biomarkers of abnormalities in brain network function (Assaf et al., 2010; Kennedy and Courchesne, 2008). To date, most research on functional brain networks, including the default mode network, has relied on functional MRI. Networks determined by fMRI reflect only the hemodynamic or metabolic response of neurons (Power et al., 2010). This can be considered a kind of amplitude correlation but not true synchronization of brain regions. If synchronization of electrical activity can be shown to be an alternative measure of brain network activity it would open up much more exploration of the role of brain networks in cognitive activity, brain computer interfaces and neuropsychiatric disorders. Aberrations to default mode networks have been implicated in a number of brain disorders (Broyd et al., 2009) including post traumatic stress syndrome (Daniels et al., 2010), social phobias (Gentili et al., 2009), depression (Sheline et al., 2009), ADHD (Uddin et al., 2008), autism (Di Martino et al., 2009), and schizophrenia (Lagioia et al., 2010). fMRI is far too expensive to be used routinely as a clinical screening and monitoring tool. Yet the apparent widespread role of synchronized brain networks in many neuropsychiatric disorders suggests that a less expensive and easy to administer technology for analyzing brain networks would be widely

Several univariate measures of time series complexity and a number of approaches for computing the degree of synchronization between signals have been used to analyze EEG data. Applications of these methods to psychiatric care and mental health continue to show promise. A more general framework for characterizing the dynamics of complex systems may be to construct recurrence plots (Marwan et al., 2007) and compute quantitative properties. The idea to use recurrence plots as a representation of complex system dynamics was first proposed by (Eckmann et al., 1987) in the late 1980s. The original tool presented a graphical means for visualizing differences in system dynamics. Methods for quantifying the small scale structures in recurrence plots were devised and shown to be capable of revealing system parameters and transitions that are not easily obtained by other methods (Marwan et al., 2002; Zbilut et al., 2002). Some dynamical parameters, such as K2 entropy and mutual information can also be derived from recurrence plots without RQA methods by computing the distribution of line lengths in the recurrence plot (Marwan et al., 2007). Readers are referred to the references for reviews of this unifying approach to nonlinear

synchronization.

useful in clinical practice.

systems analysis.

**3.3 Recurrence quantitative analysis** 
