Research on Attention EEG Based on Jensen-Shannon Divergence

2014 ◽  
Vol 884-885 ◽  
pp. 512-515
Author(s):  
Zheng Xia Zhang ◽  
Si Qiu Xu ◽  
Er Ning Zhou ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Brain Function ◽  
Complexity Analysis ◽  
The State ◽  
Function Evaluation ◽  
Eeg Signals ◽  
Alpha Wave ◽  
Eyes Closed ◽  
Statistical Complexity ◽  
Jensen Shannon Divergence

The article adopted the Jensen - Shannon Divergence analysis method for alpha wave EEG complexity analysis, used to quantify the three different status (Eyes closed, count, idle) degree of coupling between acquisition of EEG time series. The algorithm are used to calculate the statistical complexity of alpha wave EEG signals then T test, the results show that the state of eyes closed and idle under the coupling degree between EEG time series, and the state of eyes closed and counting, counting and daze cases EEG time series have significant differences. Thus JSD algorithm can be used to analyze EEG signals attention, statistical complexity can be used as a measure of brain function parameters and would be applied to the auxiliary clinical brain function evaluation in the future.

Alpha Wave Attention EEG Analysis Based on the Multiscale Jensen-Shannon Divergence

2014 ◽  
Vol 595 ◽  
pp. 269-273
Author(s):  
Zheng Xia Zhang ◽  
Si Qiu Xu ◽  
Er Ning Zhou ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Brain Function ◽  
Complexity Analysis ◽  
Function Parameter ◽  
Function Evaluation ◽  
Eeg Signals ◽  
Alpha Wave ◽  
Eyes Closed ◽  
Statistical Complexity ◽  
Jensen Shannon Divergence

The article adopted the multiscale Jensen - Shannon Divergence method for EEG complexity analysis. Then the study found that the method can distinguish between three different status (eyes closed, count, in a daze) acquisition of Alpha EEG time series, which shows three different states of Alpha EEG time series having significant differences. In each state of the three different states (eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series proxy data, finding that there are large amounts of nonlinear time series in the Alpha EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze EEG signals. Attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

Research on Attention EEG Based on the Multiscale Jensen-Shannon Divergence

2014 ◽  
Vol 529 ◽  
pp. 675-678
Author(s):  
Zheng Xia Zhang ◽  
Si Qiu Xu ◽  
Er Ning Zhou ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Brain Function ◽  
Complexity Analysis ◽  
Function Parameter ◽  
Function Evaluation ◽  
Analysis Method ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Statistical Complexity ◽  
Jensen Shannon Divergence

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis. Then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of EEG time series. It showed that three different states of EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data. It was found that there are large amounts of nonlinear time series in the EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze attention EEG signals. The multiscale Jensen-Shannon Divergence statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

Multiscale Jensen-Shannon Divergence Based Analysis of Beta Wave Attention EEG

2014 ◽  
Vol 574 ◽  
pp. 723-727
Author(s):  
Zheng Xia Zhang ◽  
Si Qiu Xu ◽  
Er Ning Zhou ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Brain Function ◽  
Complexity Analysis ◽  
Function Parameter ◽  
Function Evaluation ◽  
Eeg Signals ◽  
Multi Scale ◽  
Eyes Closed ◽  
Statistical Complexity ◽  
Jensen Shannon Divergence

The article adopted the multiscale Jensen - Shannon Divergence analysis method for EEG complexity analysis, then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of Beta EEG time series, shows three different states of Beta EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze),we are aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data, finding that there are large amounts of nonlinear time series in the Beta EEG signals. This method is also fully proved that the multi-scale JSD algorithm can be used to analyze EEG signals, attention statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

HIGHER ORDER COMPLEXITY OF TIME SERIES

2004 ◽  
Vol 14 (08) ◽  
pp. 2979-2990 ◽  
Author(s):  
FANJI GU ◽  
ENHUA SHEN ◽  
XIN MENG ◽  
YANG CAO ◽  
ZHIJIE CAI
Keyword(s):  
Time Series ◽  
Mental Arithmetic ◽  
Higher Order ◽  
Second Order ◽  
Stationary Time Series ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Logistic Mapping ◽  
Original Time ◽  
Different Order

A concept of higher order complexity is proposed in this letter. If a randomness-finding complexity [Rapp & Schmah, 2000] is taken as the complexity measure, the first-order complexity is suggested to be a measure of randomness of the original time series, while the second-order complexity is a measure of its degree of nonstationarity. A different order is associated with each different aspect of complexity. Using logistic mapping repeatedly, some quasi-stationary time series were constructed, the nonstationarity degree of which could be expected theoretically. The estimation of the second-order complexity of these time series shows that the second-order complexities do reflect the degree of nonstationarity and thus can be considered as its indicator. It is also shown that the second-order complexities of the EEG signals from subjects doing mental arithmetic are significantly higher than those from subjects in deep sleep or resting with eyes closed.

scholarly journals Fronto-Temporal Analysis of EEG Signals of Patients with Depression: Characterisation, Nonlinear Dynamics and Surrogate Analysis

2020 ◽  
Author(s):  
Subha D. Puthankattil
Keyword(s):  
Time Series ◽  
Unipolar Depression ◽  
Temporal Analysis ◽  
Mental States ◽  
Eeg Signals ◽  
Biosignal Analysis ◽  
Eyes Closed ◽  
Eyes Open ◽  
Eeg Recordings ◽  
The Brain

The recent advances in signal processing techniques have enabled the analysis of biosignals from brain so as to enhance the predictive capability of mental states. Biosignal analysis has been successfully used to characterise EEG signals of unipolar depression patients. Methods of characterisation of EEG signals and the use of nonlinear parameters are the major highlights of this chapter. Bipolar frontopolar-temporal EEG recordings obtained under eyes open and eyes closed conditions are used for the analysis. A discussion on the reliability of the use of energy distribution and Relative Wavelet Energy calculations for distinguishing unipolar depression patients from healthy controls is presented. The potential of the application of Wavelet Entropy to differentiate states of the brain under normal and pathologic condition is introduced. Details are given on the suitability of ascertaining certain nonlinear indices on the feature extraction, assuming the time series to be highly nonlinear. The assumption of nonlinearity of the measured EEG time series is further verified using surrogate analysis. The studies discussed in this chapter indicate lower values of nonlinear measures for patients. The higher values of signal energy associated with the delta bands of depression patients in the lower frequency range are regarded as a major characteristic indicative of a state of depression. The chapter concludes by presenting the important results in this direction that may lead to better insight on the brain activity and cognitive processes. These measures are hence posited to be potential biomarkers for the detection of depression.

NONLINEAR PREDICTION AND COMPLEXITY OF ALPHA EEG ACTIVITY

2000 ◽  
Vol 10 (01) ◽  
pp. 123-133 ◽  
Author(s):  
MICHAEL E. BRANDT ◽  
AHMET ADEMOĞLU ◽  
WALTER S. PRITCHARD
Keyword(s):  
Time Series ◽  
Mean Squared Error ◽  
Chaotic Time Series ◽  
Prediction Errors ◽  
K Nearest Neighbor ◽  
Eeg Signals ◽  
Nonlinear Method ◽  
Eyes Closed ◽  
Prediction Techniques ◽  
Better Than

Two prediction techniques were used to investigate the dynamical complexity of the alpha EEG; a nonlinear method using the K-nearest-neighbor local linear (KNNLL) approximation, and one based on global linear autoregressive (AR) modeling. Generally, KNNLL has more ability to predict nonlinearity in a chaotic time series under moderately noisy conditions as demonstrated by using increasingly noisy realizations of the Hénon (a low-dimensional chaotic) and Mackey–Glass (a high-dimensional chaotic) maps. However, at higher noise levels KNNLL performs no better than AR prediction. For linear stochastic time series, such as a sine wave with added Gaussian noise, prediction using KNNLL is no better than AR even at very low signal-to-noise ratios. Both prediction techniques were applied to resting EEGs (O2 scalp recording site, 10–20 EEG system) from ten normal adult subjects under eyes-closed and eyes-open conditions. In all recordings tested, KNNLL did not yield a lower root mean squared error (RMSE) than AR prediction. This result more closely resembles that obtained for noisy sine waves as opposed to chaotic time series with added noise. This lends further support to the notion that these EEG signals are linear-stochastic in nature. However, the possibility that some EEG signals, particularly those with high prediction errors produced by a noisy nonlinear system cannot be ruled out in this study.

scholarly journals Vitamin D, SARS-CoV-2 and Causal Associations in Transversal Studies: The Time-Series Analysis to Reveal Potential Confounders. Comment on Gaudio et al. Vitamin D Levels Are Reduced at the Time of Hospital Admission in Sicilian SARS-CoV-2-Positive Patients. Int. J. Environ. Res. Public Health 2021, 18, 3491

2021 ◽  
Vol 18 (13) ◽  
pp. 6793
Author(s):  
Cristiano Ialongo ◽  
Antonella Farina ◽  
Raffaella Labriola ◽  
Antonio Angeloni ◽  
Emanuela Anastasi
Keyword(s):  
Public Health ◽  
Vitamin D ◽  
Time Series ◽  
Severe Acute Respiratory Syndrome ◽  
Time Series Analysis ◽  
Hospital Admission ◽  
The State ◽  
Series Analysis ◽  
Vitamin D Levels ◽  
Time Of Admission

We read with great interest the paper by Gaudio and colleagues on vitamin D and on the state of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the time of admission [...]

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

Multiscale Mutual Mode Entropy Based Coupling Analysis of Alpha Rhythm Electroencephalogram

2014 ◽  
Vol 574 ◽  
pp. 718-722
Author(s):  
Ning Ji ◽  
Jun Tan ◽  
An Shan Pei ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Clinical Evaluation ◽  
Brain Function ◽  
Alpha Rhythm ◽  
Physiological Status ◽  
Noise Resistance ◽  
Middle Aged ◽  
Coupling Analysis ◽  
Scale Change ◽  
The Difference

This paper presents the Multiscale Mutual Mode Entropy algorithm to quantify the coupling degree between two alpha rhythm EEG time series which are simultaneously acquired. The results show that in the process of scale change, the young and middle-aged differ from each other in terms of the coupling degree of alpha rhythm EEG and the difference grow clear gradually. So the Multiscale Mutual Mode Entropy can be used to analyze the coupling information of time series under different physiological status, and it also has good noise resistance. Besides, as an indicator of measuring brain function, in the future it can also come to the aid of clinical evaluation of brain function.

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