Special Issue: Entropy and Electroencephalography

OSVALDO A. ROSSO

MDPI

Lugar: Basel; Año: 2014 p. 200

0-8436-1072-7

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. The processing of information by the brain results in dynamical changes in its electrical activity; among thechanging variables are time, frequency, and space. Therefore, concomitant studies require methods capable of describing the qualitativeand quantitative signal variations in terms of time, frequency, and spatial localization. The traditional way of analyzing brain electrical activity, on the basis of electroencephalography (EEG) records, relies mainly on visualinspection and years of training. Although such analysis is quite useful, its subjective nature precludes a systematic protocol. Over the last few years, Information Theorybased quantifiers, such as entropy measures and related metrics, have emerged as particularly appropriate complexity measures in the study of timeseries 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 nonrandomway. 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. Entropybasedquantifiers seem particularly wellequipped 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 timeseries 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 realworld times eries and are rather robust with respect to noise sources and artifacts. Finally, realtimeapplications for clinical purposes require computationally parsimonious algorithms that can provide reliable results for relatively short and noisy timeseries. Most existing methods require long, stationary, and noiseless data. In contrast, methods utilizing quantifiers based upon entropy measure can be extremely fast and robust, and seem particularly advantageous when there are huge data sets and no time for preprocessing and finetuningparameters. For this Special Issue, we welcome submissions related to timeseriesanalysis using entropy quantifiers and related measures to studybrain (electrical) activity that is recorded under normal and special conditions (e.g., conditions induced by anesthesia or other drugs). We also welcome studies concerning pathological states (e.g., epilepsy, schizophrenia, etc.) and cognitive neuroscience. We envisage contributions that aim at clarifying brain dynamics characteristics using timeseries 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, determinism detection, detection of dynamical change prediction, and spatiotemporal dynamics.