Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Since the second half of the twentieth century, intracranial electroencephalography (iEEG),
including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG) …

We introduce in this survey the major concepts, models, and algorithms proposed so far to
infer causal relations from observational time series, a task usually referred to as causal …

Understanding how different areas of the human brain communicate with each other is a
crucial issue in neuroscience. The concepts of structural, functional and effective …

Stock market prediction has been identified as a very important practical problem in the
economic field. However, the timely prediction of the market is generally regarded as one of …

In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

In predictive coding, experience generates predictions that attenuate the feeding forward of
predicted stimuli while passing forward unpredicted “errors.” Different models have …

Oscillatory neuronal activity may provide a mechanism for dynamic network coordination.
Rhythmic neuronal interactions can be quantified using multiple metrics, each with their own …

Abstract Background Wiener–Granger causality (“G-causality”) is a statistical notion of
causality applicable to time series data, whereby cause precedes, and helps predict, effect. It …

There is a popular belief in neuroscience that we are primarily data limited, and that
producing large, multimodal, and complex datasets will, with the help of advanced data …