With the advancement in artificial intelligence (AI) and machine learning (ML) techniques,
researchers are striving towards employing these techniques for advancing clinical practice …

Many complex and interesting phenomena in nature are due to nonlinear phenomena. The
theory of nonlinear dynamical systems, also called 'chaos theory', has now progressed to a …

Epilepsy is one of the world's most common neurological diseases. Early prediction of the
incoming seizures has a great influence on epileptic patients' life. In this paper, a novel …

Epileptic seizure is the most common disorder of human brain, which is generally detected
from electroencephalogram (EEG) signals. In this paper, we have proposed the new …

We compare dynamical properties of brain electrical activity from different recording regions
and from different physiological and pathological brain states. Using the nonlinear prediction …

Electroencephalogram (EEG) signals are non-linear and non-stationary in nature. The
phase-space representation (PSR) method is useful for analysing the non-linear …

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 …

Today, neuroimaging techniques are frequently used to investigate the integration of
functionally specialized brain regions in a network. Functional connectivity, which quantifies …

Recent research has investigated the possibility of predicting epileptic seizures. Intervention
before the onset of seizure manifestations could be envisioned with accurate seizure …

Since traditional electrical brain signal analysis is mostly qualitative, the development of new
quantitative methods is crucial for restricting the subjectivity in the study of brain signals …