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The relationship of epileptic spikes to seizures has been a matter of debate with researchers reporting higher or lower spike rates before and after seizures. To elucidate a possible role of spikes in ictogenesis (seizure generation) we developed a mathematical framework to investigate the spatiotemporal dynamics of spikes in the epileptic brain. Long-term (5-10 days) intracranial electroencephalograms (iEEGs) from 2 patients with temporal lobe epilepsy who underwent presurgical evaluation for localization of candidate epileptic foci were used in the study. First, spikes were detected using an in-house spike detection algorithm. To characterize the spatiotemporal changes in spikes a novel spatial synchronization measure (SSM) of spikes was developed. Application of SSM to iEEG revealed monotonically increasing long-term preictal synchronization between spike trains at brain sites that included the epileptogenic zone. The presence of preictal synchronization of spikes hours to minutes prior to the onset of seizures indicates a possible predictive value of spike synchronization for seizure occurrence. Furthermore, across seizures and patients, we observed that desynchronization of spikes at critical brain sites mostly occurred in postictal epochs. We characterized these observed changes between preictal and postical spike synchronization by a measure of resetting based on the fraction of pairs that synchronize preictally and desynchronize postictally. On the basis of this resetting measure, we showed that resetting of spike synchronization occurs more commonly at seizures than interictally (p Keywords: Epilepsy, Spikes, Seizures, Spatiotemporal Analysis, Ictogenesis, INTRODUCTION Epilepsy is a widely prevalent neurological disorder affecting around 50 million people worldwide and has been classified as the third most common neurological disorder next to stroke and Alzheimer's. [...]

Electroencephalography (EEG) has been used for decades to measure the brain's electrical activity. Planning and performing a complex movement (e.g., reaching and grasping) requires the coordination of muscles by electrical activity that can be recorded with scalp EEG from relevant regions of the cortex. Prior studies, utilizing motion capture and kinematic measures, have shown that an augmented reality feedback system for rehabilitation of stroke patients can help patients develop new motor plans and perform reaching tasks more accurately. Historically, traditional signal analysis techniques have been utilized to quantify changes in EEG when subjects perform common, simple movements. These techniques have included measures of event-related potentials in the time and frequency domains (e.g., energy and coherence measures). In this study, a more advanced, nonlinear, analysis technique, mutual information (MI), is applied to the EEG to capture the dynamics of functional connections between brain sites. In particular, the cortical activity that results from the planning and execution of novel reach trajectories by normal subjects in an augmented reality system was quantified by using statistically significant MI interactions between brain sites over time. The results show that, during the preparation for as well as the execution of a reach, the functional connectivity of the brain changes in a consistent manner over time, in terms of both the number and strength of cortical connections. A similar analysis of EEG from stroke patients may provide new insights into the functional deficiencies developed in the brain after stroke, and contribute to evaluation, and possibly the design, of novel therapeutic schemes within the framework of rehabilitation and BMI (brain machine interface).