4,454 research outputs found
Electroencephalographic field influence on calcium momentum waves
Macroscopic EEG fields can be an explicit top-down neocortical mechanism that
directly drives bottom-up processes that describe memory, attention, and other
neuronal processes. The top-down mechanism considered are macrocolumnar EEG
firings in neocortex, as described by a statistical mechanics of neocortical
interactions (SMNI), developed as a magnetic vector potential . The
bottom-up process considered are waves prominent in synaptic
and extracellular processes that are considered to greatly influence neuronal
firings. Here, the complimentary effects are considered, i.e., the influence of
on momentum, . The canonical
momentum of a charged particle in an electromagnetic field, (SI units), is calculated, where the charge of
is , is the magnitude of the charge of an
electron. Calculations demonstrate that macroscopic EEG can be
quite influential on the momentum of ions, in
both classical and quantum mechanics. Molecular scales of
wave dynamics are coupled with fields developed at macroscopic
regional scales measured by coherent neuronal firing activity measured by scalp
EEG. The project has three main aspects: fitting models to EEG
data as reported here, building tripartite models to develop
models, and studying long coherence times of waves in the
presence of due to coherent neuronal firings measured by scalp
EEG. The SMNI model supports a mechanism wherein the interaction at tripartite synapses, via a dynamic centering
mechanism (DCM) to control background synaptic activity, acts to maintain
short-term memory (STM) during states of selective attention.Comment: Final draft. http://ingber.com/smni14_eeg_ca.pdf may be updated more
frequentl
Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals
[EN] One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.This work was supported by the Spanish Ministry of Economy and Competitiveness, the European Regional Development Fund (MCIU/AEI/FEDER, UE RTI2018-094449-A-I00-AR), and by the Generalitat Valenciana (AICO/2019/220)Nieto Del-Amor, F.; Beskhani, R.; Ye Lin, Y.; Garcia-Casado, J.; Díaz-Martínez, MDA.; Monfort-Ortiz, R.; Diago-Almela, VJ.... (2021). Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals. Sensors. 21(18):1-17. https://doi.org/10.3390/s21186071S117211
Dynamic inverse problem solution considering non-homogeneous source distribution with frequency spatio temporal constraints applied to brain activity reconstruction
Para reconstruir la actividad cerebral es necesario estimular la ubicación de las fuentes activas del cerebro. El método de localización de fuentes usando electroencefalogramas es usado para esta tarea por su alta resolución temporal. Este método de resolver un problema inverso mal planteado, el cual no tiene una solución única debido al que el números de variables desconocidas es mas grande que el numero de variables conocidas. por lo tanto el método presenta una baja resolución espacial..
Network dynamics in the healthy and epileptic developing brain
Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. EEG recordings can be summarised along different dimensions using network-level quantitative measures, e.g. channel-to-channel correlation, or band power distributions across channels. These reveal network patterns that unfold over a range of different time scales and can be tracked dynamically.
Here we describe the dynamics of network-state transitions in EEG recordings of spontaneous brain activity in normally developing infants and infants with severe early infantile epileptic encephalopathies (n=8, age: 1-8 months). We describe differences in measures of EEG dynamics derived from band power, and correlation-based summaries of network-wide brain activity.
We further show that EEGs from different patient groups and controls may be distinguishable based on a small set of the novel quantitative measures introduced here, which describe dynamic network state switching. Quantitative measures related to the sharpness of switching from one correlation pattern to another show the largest differences between groups.
These findings reveal that the early epileptic encephalopathies are associated with characteristic dynamic features at the network level. Quantitative network-based analyses like the one presented here may in future inform the clinical use of quantitative EEG for diagnosis
Genetics of human sleep EEG
Sleep characteristics are candidates for predictive biological markers in patients with severe psychiatric diseases, in particular affective disorder and schizophrenia. The genetic components of sleep determination in humans remain, to a large degree, unelucidated. In particular, the heritability of rapid eye movement (REM) sleep and EEG bursts of oscillatory brain activity in Non-REM sleep, i.e. sleep spindles, are of interest. In addition, recent findings suggest a strong role of distinct sleep spindle types in memory consolidation, making it important to identify sleep spindles in slow wave sleep (SWS) and to separate slow and fast spindle localization in the frequency range. However, predictive sleep biomarker research requires large sample sizes of healthy and affected human individuals. Therefore, the present work addressed two questions. The first aim was to optimize data analysis by developing algorithms that allow an efficient and reliable identification of rapid eye movements (REMs) and sleep EEG spindles. In the second part, developed methods were applied to sleep EEG data from a classical twin study to identify genetic effects on tonic and phasic REM sleep parameters, sleep spindles, and their trait-like characteristics.
The algorithm for REM detection was developed for standard clinical two channel electrooculographic montage. The goal was to detect REMs visible above the background noise, and in the case of REM saccades to classify each movement separately. In order to achieve a high level of sensitivity, detection was based on a first derivative of electrooculogram (EOG) potentials and two detection thresholds. The developed REM detector was then validated in n=12 polysomnographic recordings from n=7 healthy subjects who had been previously scored visually by two human experts according to standard guidelines. Comparison of automatic REM detection with human scorers revealed mean correlations of 0.94 and 0.90, respectively (mean correlation between experts was 0.91).
The developed automatic sleep spindle detector assessed individualized signal amplitude for each channel as well as slow and fast spindle frequency peaks based on the spectral analysis of the EEG signal. The spindle detection was based on Continuous Wavelet Transform (CWT); it localized the exact length of sleep spindles and was sensitive also for detection of sleep spindles intermingled in high amplitude slow wave EEG activity. The automatic spindle detector was validated in n=18 naps from n=10 subjects, where EEG data were scored both visually and by a commercial automatic algorithm (SIESTA). Comparison of our own spindle detector with results from the SIESTA algorithm and visual scoring revealed the correlations of 0.97 and 0.92, respectively (correlation between SIESTA algorithm and visual scoring was 0.90).
In the second part of the work, the similarity of given sleep EEG parameters in n=32 healthy monozygotic (MZ) twins was compared with the similarity in n=14 healthy same-gender dizygotic (DZ) twins. The author of the current work did not participate in acquisition of twin study sample. EEG sleep recordings used for the heritability study were collected and already described by Ambrosius et al. (2008). Investigation of REM sleep included the absolute EEG spectral power, the shape of REM power spectrum, the amount and the structural organization of REMs; parameters of Non-REM sleep included slow and fast sleep spindle characteristics as well as the shape of the Non-REM power spectrum in general. In addition to estimating genetic effects, differences in within-pair similarity and night-to-night stability of given parameters were illustrated by intraclass correlation coefficients (ICC) and cluster analysis. A substantial genetic influence on both spectral composition and phasic parameters of REM sleep was observed. A significant genetic variance in spectral power affected delta to high sigma and high beta to gamma EEG frequency bands, as well as all phasic REM parameters with the exception of the REMs occurring outside REM bursts. Furthermore, MZ and DZ twins differed significantly in their within-pair similarity of non-REM and REM EEG spectra morphology. Regarding sleep spindles, statistical analysis revealed a significant genetic influence on localization in frequency range as well as on basic spindle characteristics (amplitude, length, quantity), except in the quantity of fast spindles in stage 2 and whole Non-REM sleep. Basic spindle parameters showed trait-like characteristics and significant differences in within-pair similarity between the twin groups.
In summary, the developed algorithms for automatic REM and sleep spindle detection provide several advantages: the elimination of human scorer biases and intra-rater variability, investigation of structural organization of REMs, exact determination of fast and slow spindle frequency for each individual. Algorithms are fully automated and therefore well suited to score REM density and sleep spindles in large patient samples. In the second part of the study, sleep EEG analysis in MZ and DZ twins revealed a substantial genetic determination of both tonic and phasic REM sleep parameters. This complements previous findings of a high genetic determination of the Non-REM sleep power spectrum. Interestingly, smaller genetic effects and lower night-to-night stability were observed for fast spindles, especially in SWS. This is in line with recent hypotheses on the differential function of sleep spindle types for memory consolidation.
The results from the presented studies strongly support the application of sleep EEG to identify clinically relevant biomarkers for psychiatric disorders
Towards Real-World BCI: CCSPNet, A Compact Subject-Independent Motor Imagery Framework
A conventional subject-dependent (SD) brain-computer interface (BCI) requires
a complete data-gathering, training, and calibration phase for each user before
it can be used. In recent years, a number of subject-independent (SI) BCIs have
been developed. However, there are many problems preventing them from being
used in real-world BCI applications. A weaker performance compared to the
subject-dependent (SD) approach, and a relatively large model requiring high
computational power are the most important ones. Therefore, a potential
real-world BCI would greatly benefit from a compact low-power
subject-independent BCI framework, ready to be used immediately after the user
puts it on. To move towards this goal, we propose a novel subject-independent
BCI framework named CCSPNet (Convolutional Common Spatial Pattern Network)
trained on the motor imagery (MI) paradigm of a large-scale
electroencephalography (EEG) signals database consisting of 21600 trials for 54
subjects performing two-class hand-movement MI tasks. The proposed framework
applies a wavelet kernel convolutional neural network (WKCNN) and a temporal
convolutional neural network (TCNN) in order to represent and extract the
diverse spectral features of EEG signals. The outputs of the convolutional
layers go through a common spatial pattern (CSP) algorithm for spatial feature
extraction. The number of CSP features is reduced by a dense neural network,
and the final class label is determined by a linear discriminative analysis
(LDA) classifier. The CCSPNet framework evaluation results show that it is
possible to have a low-power compact BCI that achieves both SD and SI
performance comparable to complex and computationally expensive.Comment: 15 pages, 6 figures, 6 tables, 1 algorith
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