Dear Colleagues,

Synchronous neuronal discharges create rhythmic potential fluctuations that can be recorded from the scalp through electroencephalography. The electroencephalogram (EEG) can be roughly defined as the mean brain electrical activity measured at different sites of the head. An EEG reflects characteristics of the brain activity itself and also yields clues concerning the underlying associated neural dynamics. Information processing in the brain involves neurons communicating with each other and results in dynamical changes in their electrical activity. Relevant dynamical changes during information processing are also reflected in the time series, frequency, and through different brain localizations. Therefore, concomitant studies require methods capable of describing the qualitative and quantitative signal variations in time, frequency, and spatial localization.

The traditional way of analyzing brain electrical activity, on the basis of electroencephalography (EEG) records, relies mainly on visual inspection and years of training. Although such analysis is quite useful, its subjective nature precludes a systematic protocol.

Over the last few years, complex networks theory gained wider applicability since methods for transformation of time series to networks have been proposed and successfully tested, expanding in this way the form in which we analyze and characterize time series. In addition, Information Theory based quantifiers, such as entropy measures and related metrics, have emerged as particularly appropriate complexity measures in the study of time series from biological systems (such as the brain). The reasons for this increasing success are manifold.

First, biological systems are typically characterized by complex dynamics. Even at rest, such systems’ dynamics have rich temporal structures. On the one hand, spontaneous brain activity encompasses a set of dynamically switching states, which are continuously reedited across the cortex, in a non random way. On the other hand, various pathologies are associated with the appearance of highly stereotyped patterns of activity. For instance, epileptic seizures are typically characterized by ordered sequences of symptoms. Entropy based quantifiers seem particularly well equipped to capture these structures (i.e., stereotyped patterns) in both healthy systems and in pathological states.

Second, while over the last few decades, a wealth of linear (and, more recently, nonlinear) methods for quantifying these structures from time series have been devised, most of them, in addition to making restrictive hypotheses as to the type of underlying dynamics, are vulnerable to even low levels of noise. Even mostly deterministic biological time series typically contain a certain degree of randomness (e.g., in the form of dynamical and observational noise). Therefore, analyzing signals from such systems necessitates methods that are model free and robust. Contrary to most nonlinear measures, some entropy measures and derived metrics can be calculated for arbitrary real world time series and are rather robust with respect to noise sources and artifacts, and can be used in order to extract information between simultaneous recording data (causality, transfer information, synchronicity, etc.).

Finally, real time applications for clinical purposes require computationally parsimonious algorithms that can provide reliable results for relatively short and noisy time series. Most existing methods require long, stationary, and noiseless data. In contrast, methods utilizing quantifiers based on Information Theory, such as entropy measures, can be extremely fast and robust, and seem particularly advantageous when there are huge datasets and no time for preprocessing and fine tuning parameters. These new quantifiers can be applied to one-dimensional time series, as well as, adapted for complex networks.

For this second Special Issue on "Entropy and EEG", we welcome submissions related to time series analysis using entropy quantifiers and related measures to study brain (electrical) dynamics that is recorded under normal and special conditions like, sleep, conditions induced by anesthesia or other drugs. We also welcome studies concerning major abnormalities (pathological states) such as epilepsy seizures and mental illnesses such as dementia, schizophrenia, Alzheimer's and Parkinson's disease; and cognitive neuroscience, as well as computer-brain interphase. We envisage contributions that aim at clarifying brain dynamics characteristics using time series recorded with electroencephalographic (EEG) techniques. In addition, we hope to receive original papers illustrating entropic methods' wide variety of applications, which are relevant for studying EEG classification, EEG and its relation with local field potentials (LFP), determinism detection, detection of dynamical changes, prediction and spatiotemporal dynamics.

Dr. Osvaldo A. Rosso
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

• brain dynamics
• EEG and LFP time series
• wavelet analysis
• permutation entropy
• permutation statistical complexity
• approximate entropy
• sample entropy
• complex networks
• synchronization
• sleep
• epilepsy
• schizophrenia, Alzheimer's and Parkinson's disease