Special Issue "Entropy and Electroencephalography"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (31 July 2014)
Dr. Osvaldo Anibal Rosso
Laboratory of Complex Systems, Faculty of Engineering, University of Buenos Aires, Autonomous City of Buenos Aires, Argentina and Institute of Physics, Federal University of Alagoas, Maceió, Alagoas, Argentina
Interests: time-series analysis; information theory; time-frequency transform; wavelet transform; entropy and complexity; non-linear dynamics and chaos; biological applications
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 the changing variables are time, frequency, and space. Therefore, concomitant studies require methods capable of describing the qualitative and 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 visual inspection and years of training. Although such analysis is quite useful, its subjective nature precludes a systematic protocol.
Over the last few years, 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 re-edited 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.
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 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 fine-tuning parameters.
For this Special Issue, we welcome submissions related to time-series analysis using entropy quantifiers and related measures to study brain (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 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, determinism detection, detection of dynamical change prediction, and spatio-temporal dynamics.
Dr. Osvaldo A. Rosso
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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed Open Access monthly journal published by MDPI.
Entropy 2014, 16(8), 4677-4692; doi:10.3390/e16084677
Received: 21 July 2014; Accepted: 20 August 2014 / Published: 21 August 2014| PDF Full-text (615 KB)
Entropy 2014, 16(8), 4603-4611; doi:10.3390/e16084603
Received: 24 June 2014; in revised form: 16 July 2014 / Accepted: 12 August 2014 / Published: 18 August 2014| PDF Full-text (695 KB)
Entropy 2014, 16(6), 3049-3061; doi:10.3390/e16063049
Received: 14 April 2014; in revised form: 26 May 2014 / Accepted: 27 May 2014 / Published: 30 May 2014| PDF Full-text (614 KB) | HTML Full-text | XML Full-text
Last update: 18 February 2014