The increasingly sophisticated methods for measuring and visualizing brain activity have opened up important areas of basic research in psychiatry, psychology, and neurology. These new neuroimaging modalities pose new opportunities and challenges for the signal processing community. One such challenge is the quantification of the interactions between signals recorded at different sites, also known as the functional integration in the brain. The current imaging modalities do not provide a measure of the functional interaction between electrical or magnetic activity recorded at different sites. In order to gain a better understanding of how the brain processes information, it is crucial to quantify these interactions between its subsystems. This research involves developing two complementary signal processing methods for quantifying the connectivity patterns in the brain based on the electroencephalogram (EEG) recordings.
The investigator develops two types of time-varying measures of connectivity to quantify the functional integration in the brain: 1) Time-varying measures of coherence that separate the effects of amplitude correlation and phase synchrony from each other and quantify the synchrony between pairs of signals; 2) Information-theoretic measures on the time-frequency plane that quantify the complexity of individual signals, as well as the interdependence between pairs of signals. These measures are applied to EEG data sets, in particular to the study of psychopathologies such as schizophrenia. The investigator's approach offers significant improvements over the conventional EEG processing techniques since a number of factors crucial to understanding the dynamics of brain signaling, i.e. time, frequency, phase, space and information flow, are integrated into one joint representation.
