The application of novel approaches to the analysis of physiological signals will likely lead to identification of genes affecting distinct aspects of the brain function. In the past decade, advanced methods for the analysis of spontaneous and event-related EEG activity have received widespread acceptance, such as principal- and independent-component analyses, time-frequency decomposition of event-related oscillations, measures of dynamical complexity, assessment of local neural synchrony using phase-locking measures, methods for the assessment of functional connectivity, including directional and causal measures such as Granger causality, source-level connectivity, and other advanced techniques that allow researchers to extract information about conceptually important aspects of brain functioning. Collectively, these measures allow for a better utilization of the rich information contained in the EEG and ERP data. Recent studies underscore the importance of various forms of neural synchrony as biological substrate for cognitive processing, including such measures as event-related coherence, phase-locking, and cross-frequency coupling. However, these task-related characteristics have been little studied from the genetic perspective. So far genetic studies of EEG-based connectivity measures were mostly limited to the resting state. Resting EEG characteristics have obvious
