The modeling of dynamic behavior of systems is a ubiquitous problem in all facets of human
endeavors. Importantly so, dynamical systems have been studied and modeled since the …

This paper presents a novel patient-specific algorithm for prediction of seizures in epileptic
patients. Spectral power features, including relative spectral powers and spectral power …

Electroencephalography (EEG) is an important neurophysiological modality for
understanding brain functions and disorders. Topological signal processing allows us to …

Background and Objectives: Many biological processes are modulated by rhythms on
circadian and multidien timescales. In focal epilepsy, various seizure features, such as …

More than 50 million individuals are affected by epilepsy, a chronic neurological disorder
characterized by unprovoked, recurring seizures and psychological symptoms. Researchers …

However, there are various techniques like Empirical mode decomposition (EMD), wavelet
transform, tensors, entropy, chaos theory, and dynamic analysis which are used in the area …

About 1% of the world population suffer from Epilepsy. Epileptic seizures are generated by
excessive and abnormal activation of neurons in the cortex. Unpredictable nature of these …

The desirability of accurate seizure forecasts is well understood by most individuals living
with epilepsy. Seizure forecasting could be invaluable in a closed‐loop neuromodulation …

I argue that the standard paradigm for understanding cognition—namely, that thoughts are
representational, internal, and propositional—does not account for a large number of …

There is a growing interest in data-analytic modeling for prediction and/or detection of
epileptic seizures from EEG recording of brain activity [1-10]. Even though there is clear …