scholarly journals Time series analysis of trial-to-trial variability of MEG power spectrum during rest state, unattented listening and frequency-modulated tones classification

2021 ◽  
Author(s):  
Lech Kipiński ◽  
Wojciech Kordecki
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Power Spectrum ◽  
Spectral Density ◽  
Cognitive Task ◽  
Clinical Neuroscience ◽  
Rest State ◽  
Short Time ◽  
Low Complex ◽  
Dominant Frequencies

AbstractThe nonstationarity of EEG/MEG signals is important for understanding the functioning of human brain. From the previous research we know that even very short, i.e. 250—500ms MEG signals are variance-nonstationary. The covariance of stochastic process is mathematically associated with its spectral density, therefore we investigate how the spectrum of such nonstationary signals varies in time.We analyze the data from 148-channel MEG, that represent rest state, unattented listening and frequency-modulated tones classification. We transform short-time MEG signals to the frequency domain using the FFT algorithm and for the dominant frequencies 8—12 Hz we prepare the time series representing their trial-to-trial variability. Then, we test them for level- and trend-stationarity, unit root, heteroscedasticity and gaussianity and based on their properties we propose the ARMA-modelling for their description.The analyzed time series have the weakly stationary properties independently of the functional state of brain and localization. Only their small percentage, mostly related to the cognitive task, still presents nonstationarity. The obtained mathematical models show that the spectral density of analyzed signals depends on only 2—3 previous trials.The presented method has limitations related to FFT resolution and univariate models, but it is not computationally complicated and allows to obtain a low-complex stochastic models of the EEG/MEG spectrum variability.Although the physiological short-time MEG signals are in principle nonstationary in time domain, its power spectrum at the dominant frequencies varies as weakly stationary stochastic process. Described technique has the possible applications in prediction of the EEG/MEG spectral properties in theoretical and clinical neuroscience.

EVIDENCE FOR A FRACTAL STOCHASTIC PROCESS UNDERLYING MEASLES EPIDEMICS IN BRITAIN

Fractals ◽  
2000 ◽  
Vol 08 (01) ◽  
pp. 29-34 ◽  
Author(s):  
WAYNE S. KENDAL
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Spectral Density ◽  
Geometric Distribution ◽  
Fano Factor ◽  
Distribution Histogram ◽  
Power Spectral ◽  
Allan Factor ◽  
Measles Incidence ◽  
Vaccination Period

The variability in measles incidence during the pre-vaccination period of 1944 to 1966, as recorded from 366 communities in England and Wales, was examined for properties of fractal stochastic processes. The power spectral density, Fano factor, and Allan factor were computed from the incidence time-series, and all revealed power-law scaling. As well, the distribution histogram for the weekly incidence approximated a geometric distribution. These features constituted evidence for a fractal stochastic process with underlying geometric statistics at play in the development and resolution of measles epidemics.

CONVERGENCE IN DISTRIBUTION OF THE PERIODOGRAM OF CHAOTIC PROCESSES

2002 ◽  
Vol 02 (04) ◽  
pp. 609-624 ◽  
Author(s):  
ARTUR O. LOPES ◽  
SÍLVIA R. C. LOPES
Keyword(s):  
Stochastic Process ◽  
Time Series ◽  
Fixed Point ◽  
Continuous Function ◽  
Spectral Density ◽  
Stationary Process ◽  
Spectral Density Function ◽  
Convergence In Distribution ◽  
Continuous Transformation ◽  
Distribution Sense

In this work we analyze the convergence in distribution sense of the periodogram function (to the spectral density function) based on a time series of a stationary process Xt = (φ ◦ Tt)(X0) obtained from the iterations of a continuous transformation T invariant for an ergodic probability μ and a continuous function φ taking values in ℝ. We only assume a certain rate of convergence to zero for the autocovariance coefficient of the stochastic process, i.e. we assume there exist C > 0 and β > 2 such that |γX(h)| ≤ C|h|-β, for all h ∈ ℕ, where γX(h) = ∫(φ ◦ Th)(x) φ(x)dμ(x) - (∫ φ(x)dμ(x))2 is the h-autocovariance of the process. Our result applies to the case of exponential decay of correlation (or covariance), as it happens for a continuous expanding transformation T on the circle and a Holder potential φ. It can also be applied to the case when the transformation T has a fixed point with derivative equal to one.

scholarly journals Identifying bidirectional total and non-linear information flow in functional corticomuscular coupling during a dorsiflexion task: a pilot study

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Tie Liang ◽  
Qingyu Zhang ◽  
Xiaoguang Liu ◽  
Bin Dong ◽  
Xiuling Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Information Flow ◽  
Gamma Band ◽  
Motor Dysfunction ◽  
Beta Band ◽  
Short Time Series ◽  
Information Interaction ◽  
Stroke Group ◽  
Short Time ◽  
Maximal Information Coefficient

Abstract Background The key challenge to constructing functional corticomuscular coupling (FCMC) is to accurately identify the direction and strength of the information flow between scalp electroencephalography (EEG) and surface electromyography (SEMG). Traditional TE and TDMI methods have difficulty in identifying the information interaction for short time series as they tend to rely on long and stable data, so we propose a time-delayed maximal information coefficient (TDMIC) method. With this method, we aim to investigate the directional specificity of bidirectional total and nonlinear information flow on FCMC, and to explore the neural mechanisms underlying motor dysfunction in stroke patients. Methods We introduced a time-delayed parameter in the maximal information coefficient to capture the direction of information interaction between two time series. We employed the linear and non-linear system model based on short data to verify the validity of our algorithm. We then used the TDMIC method to study the characteristics of total and nonlinear information flow in FCMC during a dorsiflexion task for healthy controls and stroke patients. Results The simulation results showed that the TDMIC method can better detect the direction of information interaction compared with TE and TDMI methods. For healthy controls, the beta band (14–30 Hz) had higher information flow in FCMC than the gamma band (31–45 Hz). Furthermore, the beta-band total and nonlinear information flow in the descending direction (EEG to EMG) was significantly higher than that in the ascending direction (EMG to EEG), whereas in the gamma band the ascending direction had significantly higher information flow than the descending direction. Additionally, we found that the strong bidirectional information flow mainly acted on Cz, C3, CP3, P3 and CPz. Compared to controls, both the beta-and gamma-band bidirectional total and nonlinear information flows of the stroke group were significantly weaker. There is no significant difference in the direction of beta- and gamma-band information flow in stroke group. Conclusions The proposed method could effectively identify the information interaction between short time series. According to our experiment, the beta band mainly passes downward motor control information while the gamma band features upward sensory feedback information delivery. Our observation demonstrate that the center and contralateral sensorimotor cortex play a major role in lower limb motor control. The study further demonstrates that brain damage caused by stroke disrupts the bidirectional information interaction between cortex and effector muscles in the sensorimotor system, leading to motor dysfunction.

The Success of Cognitive Activity of Students of the Northern (Arctic) Federal University with Different Levels of Anxietyin Different Time Conditions

2021 ◽  
Vol 18 (1) ◽  
pp. 52-59
Author(s):  
A.I. Taleeva ◽  
◽  
I.T. Madumarova ◽  
N.V. Zvyagina ◽  
◽  
...  
Keyword(s):  
Emotional State ◽  
Cognitive Task ◽  
Cognitive Activity ◽  
Whole Body ◽  
Modern World ◽  
Short Period ◽  
Personal Anxiety ◽  
Psychophysiological Testing ◽  
Short Time ◽  
Different Levels

The dynamic development of the modern world requires the processing and development of a large enough amount of information in a short period of time, which leads to a violation of the psychophysiological and psycho-emotional balance of the person. Violation of the psycho-emotional state leads to the development of increased anxiety. Students need to learn a lot of information in a very short time. The time limit affects students as a stress factor, leads to increased stress and therefore negatively affects the quality of work and in general on the whole body. The aim of the study is to determine the success of cognitive tasks by students of the Northern (Arctic) Federal University with different levels of anxiety in different time conditions. The study used a psychophysiological testing device to determine the level of situational and personal anxiety, to assess the psycho-emotional state used the technique of simple visual-motor reaction, to determine the success of the cognitive task were presented words with one missing letter.

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

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