2,757 research outputs found
Generalized Information Equilibrium Approaches to EEG Sleep Stage Discrimination
Recent advances in neuroscience have raised the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is via power-law distributed neuronal avalanches, while EEG signals are nonstationary. Therefore, spectral analysis of EEG may miss many properties inherent in such signals. A complete understanding of such dynamical systems requires knowledge of the underlying nonequilibrium thermodynamics. In recent work by Fielitz and Borchardt (2011, 2014), the concept of information equilibrium (IE) in information transfer processes has successfully characterized many different systems far from thermodynamic equilibrium. We utilized a publicly available database of polysomnogram EEG data from fourteen subjects with eight different one-minute tracings of sleep stage 2 and waking and an overlapping set of eleven subjects with eight different one-minute tracings of sleep stage 3. We applied principles of IE to model EEG as a system that transfers (equilibrates) information from the time domain to scalp-recorded voltages. We find that waking consciousness is readily distinguished from sleep stages 2 and 3 by several differences in mean information transfer constants. Principles of IE applied to EEG may therefore prove to be useful in the study of changes in brain function more generally
Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 165, March 1977
This bibliography lists 198 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1977
Automatic Detection of Eye Blinking Using the Generalized Ising Model
Electroencephalogram (EEG) is a widely used technique to record electrical brain activity. It is prone to be contaminated by non-neuronal sources that can generate artifacts in the signal due to its sensitivity and its poor signal-to-noise ratio. One of the main challenges in analyzing EEG data is the systematical and effective removal of artifacts from the signal. Although many methods have already been introduced to approach this issue, there is still no robust method for handling all sources of contaminations. For example, eye blinking is a physiological artifact occurring very frequently in spontaneous EEG recordings and therefore, removing these artifacts in a systematic way is a compelling need. The aim of this research is to build an automated pipeline to detect eye blinking artifacts in EEG signals using the generalized Ising model to act as a pattern recognition algorithm. A sample blink pattern is extracted from a single subject whose blink events are validated and marked by an EEG expert. The generalized Ising Model Algorithm works as a fully automated method for identifying all epochs similar to the eye blink pattern. Using the proposed method to discriminate the blinks artifact in continuous EEG data yields optimistic results. From eight healthy subjects, the results show high level of accuracy (90.5 %)
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Effects of dance therapy on balance, gait and neuro-psychological performances in patients with Parkinson's disease and postural instability
Postural Instability (PI) is a core feature of
Parkinsonās Disease (PD) and a major cause of falls and disabilities. Impairment of executive functions has been called as an aggravating factor on motor performances. Dance therapy has been shown effective for improving gait and has been suggested as an alternative rehabilitative method.
To evaluate gait performance, spatial-temporal (S-T) gait
parameters and cognitive performances in a cohort of patients with PD and PI modifications in balance after a cycle of dance therapy
Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 140
This bibliography lists 306 reports, articles, and other documents introduced into the NASA scientific and technical information system in March 1975
Advances in Clinical Neurophysiology
Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests
Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)
This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988
Aerospace medicine and biology: A continuing bibliography with indexes, supplement 125
This special bibliography lists 323 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1974
Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis
Multivariate Entropy quantification algorithms are becoming a prominent tool
for the extraction of information from multi-channel physiological time-series.
However, in the analysis of physiological signals from heterogeneous organ
systems, certain channels may overshadow the patterns of others, resulting in
information loss. Here, we introduce the framework of Stratified Entropy to
prioritize each channels' dynamics based on their allocation to respective
strata, leading to a richer description of the multi-channel time-series. As an
implementation of the framework, three algorithmic variations of the Stratified
Multivariate Multiscale Dispersion Entropy are introduced. These variations and
the original algorithm are applied to synthetic time-series, waveform
physiological time-series, and derivative physiological data . Based on the
synthetic time-series experiments, the variations successfully prioritize
channels following their strata allocation while maintaining the low
computation time of the original algorithm. In experiments on waveform
physiological time-series and derivative physiological data, increased
discrimination capacity was noted for multiple strata allocations in the
variations when benchmarked to the original algorithm. This suggests improved
physiological state monitoring by the variations. Furthermore, our variations
can be modified to utilize a priori knowledge for the stratification of
channels. Thus, our research provides a novel approach for the extraction of
previously inaccessible information from multi-channel time series acquired
from heterogeneous systems
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