EEG analysis of brain dynamical behavior with applications in epilepsy

Electroencephalography (EEG) is a technology for measuring the activation of neurons and is used to investigate various pathological conditions of the brain. Epilepsy is a common brain disorder that disables a patient with unforeseen seizures. One salient characteristic of epileptic EEG data is the synchronous neuronal activation over an excessive portion of a brain. My studies sought methodologies to define epilepsy-related interaction between brain regions so that the development of epileptic activity can be monitored and warned or intervened. Other findings in my studies may be used to assist epileptic foci localization and epileptic patient classification.;To forecast the coming of seizures, features were extracted from EEG data. Forecasting has been extensively and successfully studied for stationary time series. However, the non-stationarity of EEG signals tarnishes many convenient properties of a stationary time series. To overcome this complication and achieve successful seizure warnings, I used a signal-regularity-based dynamic feature and T-index to monitor the entrainment process among brain areas utilizing EEGs recorded from epileptic patients. The hypothesis was that the regularity entrainment among certain brain sites precedes seizure onsets. An algorithm was proposed and implemented on preprocessed EEG signals. The evaluation included a comparison between the proposed algorithm and a naive random scheme. The combined p-value over 20 cross-validation trials showed that the proposed warning algorithm achieved a better performance ( p=0.015) than the proposed random warning scheme.;A brain can be viewed as a complex network of neurons. A brain functional network represents quantitative interactions amongst EEG channels and can be expressed as a graph. Graph theoretical analysis, therefore, can be applied to offer a broader scope to inspect the global functional network characteristics of epileptic brains and can reveal the existence of small-world network structure. I further inspected the inter-hemispheric power asymmetry of physiologically and psychologically epileptic brains and found significant differences between the two patient groups. The degrees of asymmetry of the two patient groups differed around the frontal lobe in the delta, theta, alpha and gamma frequency bands.