Special Issue "Entropy and Electroencephalography II"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: 30 June 2017
Dr. Osvaldo Anibal Rosso
1. Departamento de Bioingeniería, Insitituto Tecnológico de Buenos Aires (ITBA), C1106ACD Av. Eduardo Madero 399, Ciudad Autónoma de Buenos Aires, Argentina
2. Instituto de Física, Universidade Federal de Alagoas (UFAL), BR 104 Norte km 97, 57072-970 Maceió, Alagoas, Brazil
3. Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Los Andes, Santiago, Chile
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. 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
Submission 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.
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.
- brain dynamics
- EEG and LFP time series
- wavelet analysis
- permutation entropy
- permutation statistical complexity
- approximate entropy
- sample entropy
- complex networks
- schizophrenia, Alzheimer's and Parkinson's disease
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Quantifying the preparation to switch: Applying information theory to a cued-trials task-switching paradigm
Authors: Patrick S Cooper, Francisco Barceló & Frini Karayanidis
Abstract: Event-related potentials (ERPs) recorded during task-switching show a distinct posterior positive component when preparing to switch task. The neural processes that underlie this ‘switch positivity’ are not well defined. For instance, it remains unclear whether this positivity represents a unique ERP component representing a switch-specific process or results from modulation of the ubiquitous P300. We will address this question by using a simple information theoretical model of cognitive control to operationalize processing in a cued-trials task-switching paradigm. This formal tool can quantify low- and high-order sensorimotor (S-R) information transmission within a putative hierarchy of frontoparietal control processes (cf., Cooper et al., Neuroimage 2016). We will apply these estimates to characterize factors associated with switch-specific and general processes contributing to the ‘switch positivity’.
Tentative title: Chess experience and EEG brain cortical organisation: an analysis using entropy, multivariate statistics and Loreta sources
Author: Fabio Rocha, Fabio Cesar, Gilson Giraldi and Carlos Thomaz
Affiliation: Electrical Engineering Department, University Center of FEI, São Bernardo do Campo-SP, 09850-901, Brazil
Abstract: Chess game has been used as a rich environment to study human cognition and several works in the neuroscientific domain have been done using different brain mapping techniques. Here, we used electroencephalogram and a brain mapping technique that involves correlation between electrodes signals, entropy computation and factor analysis to disclose the possible differences in cortical organisation of individuals, having different chess proficiency, during chess problems solving. We also used Loreta Analysis to look for the possible sources of the electrical activity. Volunteers were grouped into two different stages according to their performance, classified as beginner and experienced. Our brain mappings suggest that beginners may rely more on the linguistic informations that were presented by the question and on the visuo spatial processing of the chessboard. On the other hand, the experienced group also discloses visuo spatial regions on their maps but seems to recruit the executive functions operated by frontal areas.
Author: Karsten Keller
Title: Permutation entropy: New ideas and challenges
Abstract: During the last years new variants of permutation entropy have been introduces and applied to EEG analysis including such also using some amount of metric information and such based on entropies different from the Shannon entropy. In some situations it is not completely clear what information the new measures and their algorithmic implementation provide. We discuss the new developements from the conceptional and theoretical viewpoint and illustrate them for EEG data.
Title: Healthcare Teams Neurodynamically Reorganize when Resolving Uncertainty
Authors: Ronald H. Stevens 1, Trysha Galloway 2, Donald Halpin 3, and Ann Willemsen-Dunlap 3
Abstract: While research is revealing the microscale dynamics of social interactions the impact of these studies on the assembly, training and evaluation of teams has been small. This is partially due to the scale of neural involvements in team activities, spanning the millisecond oscillations in individual brains, to the minutes / hours performance behaviors of the team. We have used intermediate neurodynamic representations to show that healthcare teams enter persistent (50-100s) neurodynamic states when they encounter and resolve uncertainty while managing simulated patients. Each second symbols were developed situating the electroencephalogram (EEG) power of each team member in the contexts of those of other team members and the task. These representations were generated for each of the 1Hz-40Hz frequencies from 19 sensor EEG headsets. Quantitative estimates of the information in each symbol stream were calculated from a 60s moving window of Shannon entropy that was updated each second; providing a quantitative neurodynamic history of the team’s performance. Neurodynamic organizatons fluctuated with the task demands with increased organization (i.e. lower entropy) occurring when the team needed to resolve uncertainty. These results show that intermediate neurodynamic representations can provide a quantitative bridge between the micro and macro scales of teamwork.