Hilbert Vibration Decomposition-based epileptic seizure prediction with neural network.

Epilepsy is one of the most prominent brain disorders in the world, and epileptic patients suffer from sudden seizures that have a substantial negative impact on their lives. A seizure prediction system, therefore, is essential in overcoming the difficulties that epileptic individuals experience. This study designs and demonstrates a non-patient specific seizure prediction system that uses the Hilbert Vibration Decomposition (HVD) method on surface EEG recordings of 10 patients from the CHB-MIT database.

A signal-processing-based approach to time-varying graph analysis for dynamic brain network identification.

In recent years, there has been a growing need to analyze the functional connectivity of the human brain. Previous studies have focused on extracting static or time-independent functional networks to describe the long-term behavior of brain activity. However, a static network is generally not sufficient to represent the long term communication patterns of the brain and is considered as an unreliable snapshot of functional connectivity.

Inferring effective connectivity in the brain from EEG time series using dynamic Bayesian networks.

Effective connectivity, defined as the influence of a neuronal population on another, is known to have great significance for understanding the organization of the brain. Disruptions in the effective connectivity patterns occur in the case of neurological and psychopathological diseases. Therefore, it is important to develop models of effective brain connectivity from non-invasive neuroimaging data.