Phase-Synchronization Early Epileptic Seizure Detector VLSI Architecture

The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.

Synchronization of oscillatory EEG signals across different frequency bands is receiving waxing interest in cognitive neuroscience and neurophysiology, and cross-frequency coupling is being increasingly linked to cognitive and perceptual processes. Several methods exist to examine cross-frequency coupling, although each has its limitations, typically by being flexible only over time or over frequency. Here, a method for assessing transient cross-frequency coupling is presented, which allows one to test for the presence of multiple, dynamic, and flexible cross-frequency coupling structure over both time and frequency. The method is applied to intracranial EEG data, and strong coupling between gamma ( approximately 40-80 Hz) and upper theta ( approximately 7-9 Hz) was observed. This method might have useful applications in uncovering the electrophysiological correlates of cognitive processes.