Quantifying the functional relations between the nodes in a network based on local
observations is a key challenge in studying complex systems. Most existing time series …

We study causal discovery in a multi-environment setting, in which the functional relations
for producing the variables from their direct causes remain the same across environments …

We develop a nonparametric and online learning algorithm that estimates the triggering
functions of a multivariate Hawkes process (MHP). The approach we take approximates the …

This paper uses an information-theoretic lens to examine the use and implementation of
electroencephalography (EEG) systems for neural imaging. There is a widespread belief in …

Here, an unobtrusive, adhesive‐integrated electrode array for continuous monitoring of
stomach electric activity is introduced. This patient‐friendly, disposable peel‐and‐stick …

Epilepsy is one of the most common neurological disorders affecting about 1% of the world
population. For patients with focal seizures that cannot be treated with antiepileptic drugs …

We introduce a measure of causality that captures the functional dependencies in dynamical
systems and subsequently, define anew type of graphical model, functional dependency …

Quantifying relevant interactions between neural populations is a prominent question in the
analysis of high-dimensional neural recordings. However, existing dimension re-duction …

We present a sample path dependent measure of causal influence between time series. The
proposed causal measure is a random sequence, a realization of which enables …

Background: Manual visual inspection and analysis of electroencephalogram (EEG) signals
of patients are susceptible to the subjective influence of doctors. The introduction of GA-PSO …