Self-Correlations of Electroencephalograms

A susceptible-infected-susceptible (SIS) cellular automaton model for collective neural interactions proposed recently is revisited. In this model, neurons are simple network nodes with different states: active or firing, and quiescent. The main thesis of this approach is that the electroencephalogram (EEG) could emerge as the fluctuations in the number of firing neurons. In this framework, EEG is understood as a statistical epiphenomenon. In this paper, the mean number of active sites and the self-correlation function both in the SIS stochastic model and in elementary cellular automata (ECAs) are considered. Damped oscillatory relaxation to the stationary state is found both in the SIS model and in ECA rule 30; periodic oscillations are found for other class 3 and class 4 cellular automata. A statistical analysis of the selfcorrelations in real EEG shows that the damped oscillatory relaxations are found both in delta and alpha waves. The normalized amplitude of these correlations is predicted by cellular automata models. This reinforces the view of the brain as a highly complex cellular automata system.Acedo Rodríguez, L.; Aranda Lozano, DF. (2012). Self-Correlations of Electroencephalograms. Complex Systems. 20(4):289-303. http://hdl.handle.net/10251/67069S28930320

Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy

The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer’s disease (AD) patients using the Multiscale Entropy (MSE). The MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant difference

An empirical examination of the relation between attention and motivation in computer-based education: a modeling approach

Attention is considered a pre-requisite to achieve greater motivation in the classroom. However, empirical evidence of this relationship in educational setting is scarce since the measurement of attention requires specialized equipment such as clinical electroencephalograms (EEG) or fMR1. With the advent of portable, consumer-oriented EEG it is now possible to estimate levels of attention and shed light onto this relationship in the context of a computer-based educational setting. To that end, students (N=40) interacted for an average of 9.48 minutes (SD = .0018) with an assessment exercise in a virtual world. Participants' attention levels were monitored via a portable EEG and incorporated into an attention model capable of deciding on strategies to correct low levels of attention. The participants' motivation was assessed using a self-reported motivation questionnaire at pre-test and post-test times. The results indicated that students with higher self-reported motivation and self-reported attention answered significantly more correct answers. However, no direct evidence was found of a relation between EEG readings and self-reported attention or self-reported motivation. This suggests student's own perceptions of motivation and attention influence performance. Future work consists of defining new models of attention considering self-perceived attention and motivation as baseline as well as improving the model of attention combining EEG reading with an indication of the students' gaze

Artificial Sequences and Complexity Measures

In this paper we exploit concepts of information theory to address the fundamental problem of identifying and defining the most suitable tools to extract, in a automatic and agnostic way, information from a generic string of characters. We introduce in particular a class of methods which use in a crucial way data compression techniques in order to define a measure of remoteness and distance between pairs of sequences of characters (e.g. texts) based on their relative information content. We also discuss in detail how specific features of data compression techniques could be used to introduce the notion of dictionary of a given sequence and of Artificial Text and we show how these new tools can be used for information extraction purposes. We point out the versatility and generality of our method that applies to any kind of corpora of character strings independently of the type of coding behind them. We consider as a case study linguistic motivated problems and we present results for automatic language recognition, authorship attribution and self consistent-classification.Comment: Revised version, with major changes, of previous "Data Compression approach to Information Extraction and Classification" by A. Baronchelli and V. Loreto. 15 pages; 5 figure

Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient

Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the epileptic brain is long term anticorrelated whereas, the normal brain is more or less stochastic. Among all the techniques, used here, Hurst exponent is found very useful for characterization different cases. Conclusions: In this article, differences in characteristics for normal subjects with eyes open and closed, epileptic subjects during seizure and seizure free intervals have been shown mainly using Hurst exponent. The H shows that the brain activity of a normal man is uncorrelated in nature whereas, epileptic brain activity shows long range anticorrelation.Comment: Keywords:EEG, epilepsy, Correlation dimension, Surrogate analysis, Hurst exponent. 9 page

Emergent complex neural dynamics

A large repertoire of spatiotemporal activity patterns in the brain is the basis for adaptive behaviour. Understanding the mechanism by which the brain's hundred billion neurons and hundred trillion synapses manage to produce such a range of cortical configurations in a flexible manner remains a fundamental problem in neuroscience. One plausible solution is the involvement of universal mechanisms of emergent complex phenomena evident in dynamical systems poised near a critical point of a second-order phase transition. We review recent theoretical and empirical results supporting the notion that the brain is naturally poised near criticality, as well as its implications for better understanding of the brain

Frequency and Phase Synchronization in Neuromagnetic Cortical Responses to Flickering-Color Stimuli

In our earlier study dealing with the analysis of neuromagnetic responses (magnetoencephalograms - MEG) to flickering-color stimuli for a group of control human subjects (9 volunteers) and a patient with photosensitive epilepsy (a 12-year old girl), it was shown that Flicker-Noise Spectroscopy (FNS) was able to identify specific differences in the responses of each organism. The high specificity of individual MEG responses manifested itself in the values of FNS parameters for both chaotic and resonant components of the original signal. The present study applies the FNS cross-correlation function to the analysis of correlations between the MEG responses simultaneously measured at spatially separated points of the human cortex processing the red-blue flickering color stimulus. It is shown that the cross-correlations for control (healthy) subjects are characterized by frequency and phase synchronization at different points of the cortex, with the dynamics of neuromagnetic responses being determined by the low-frequency processes that correspond to normal physiological rhythms. But for the patient, the frequency and phase synchronization breaks down, which is associated with the suppression of cortical regulatory functions when the flickering-color stimulus is applied, and higher frequencies start playing the dominating role. This suggests that the disruption of correlations in the MEG responses is the indicator of pathological changes leading to photosensitive epilepsy, which can be used for developing a method of diagnosing the disease based on the analysis with the FNS cross-correlation function.Comment: 21 pages, 14 figures; submitted to "Laser Physics", 2010, 2

Computational mechanics: from theory to practice

In the last fifty years, computational mechanics has gained the attention of a large number of disciplines, ranging from physics and mathematics to biology, involving all the disciplines that deal with complex systems or processes. With ϵ-machines, computational mechanics provides powerful models that can help characterizing these systems. To date, an increasing number of studies concern the use of such methodologies; nevertheless, an attempt to make this approach more accessible in practice is lacking yet. Starting from this point, this thesis aims at investigating a more practical approach to computational mechanics so as to make it suitable for applications in a wide spectrum of domains. ϵ-machines are analyzed more in the robotics scene, trying to understand if they can be exploited in contexts with typically complex dynamics like swarms. Experiments are conducted with random walk behavior and the aggregation task. Statistical complexity is first studied and tested on the logistical map and then exploited, as a more applicative case, in the analysis of electroencephalograms as a classification parameter, resulting in the discrimination between patients (with different sleep disorders) and healthy subjects. The number of applications that may benefit from the use of such a technique is enormous. Hopefully, this work has broadened the prospect towards a more applicative interest