Coupling Analysis of Electroencephalogram Based on Mutual Mode Entropy

2014 ◽  
Vol 884-885 ◽  
pp. 415-418 ◽  
Author(s):  
Ning Ji ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Clinical Evaluation ◽  
Brain Function ◽  
Brain State ◽  
Coupling Analysis ◽  
Beta Rhythm ◽  
Simultaneous Acquisition ◽  
Parameter Measuring ◽  
Degree Of Coupling

This paper presents a mutual mode entropy algorithm to quantify the degree of coupling between two simultaneous acquisition of beta rhythm EEG time series. Applying the algorithm to calculate beta rhythm EEG and hypothesis testing, the results show that coupling degree was significantly different between different physiological states which indicating that mutual mode entropy can be used to analyze the coupling between the time series. The mutual mode entropy also can be parameter measuring brain state which can be applied to assist future clinical evaluation of brain function.

Coupling Analysis of Beta Rhythm Electroencephalogram Based on the Multiscale Mutual Mode Entropy

2014 ◽  
Vol 595 ◽  
pp. 295-300
Author(s):  
Ning Ji ◽  
Jun Tan ◽  
An Shan Pei ◽  
Jia Fei Dai ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Clinical Evaluation ◽  
Brain Function ◽  
Physiological Status ◽  
Middle Aged ◽  
Coupling Analysis ◽  
Beta Rhythm ◽  
Scale Change ◽  
The Future ◽  
The Difference

This paper presents the Multiscale Mutual Mode Entropy algorithm to quantify the coupling degree between two beta 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 beta rhythm EEG and the difference grow clear gradually from 4th scale. So the Multiscale Mutual Mode Entropy can be used to analyze the coupling information of time series under different physiological status. Besides, as an indicator of measuring brain function, in the future it can also come to the aid of clinical evaluation of brain function.

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.

scholarly journals Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

2021 ◽  
Vol 13 (10) ◽  
pp. 2006
Author(s):  
Jun Hu ◽  
Qiaoqiao Ge ◽  
Jihong Liu ◽  
Wenyan Yang ◽  
Zhigui Du ◽  
...  
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Surface Deformation ◽  
A Priori ◽  
Least Square ◽  
Deformation Model ◽  
Phase Time ◽  
Testing Theory ◽  
Sbas Insar ◽  
Deformation Models

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

scholarly journals A Speckle Filtering Method Based on Hypothesis Testing for Time-Series SAR Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1383 ◽  
Author(s):  
Jili Yuan ◽  
Xiaolei Lv ◽  
Rui Li
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Speckle Noise ◽  
Sar Images ◽  
Suppression Effect ◽  
Filtering Method ◽  
Information Preservation ◽  
Spatiotemporal Information ◽  
Test Algorithm ◽  
Filtering Effect

To improve the suppression effect for the speckle noise of synthetic aperture radar (SAR) images and the ability of spatiotemporal information preservation of the filtered image without losing the spatial resolution, a novel multitemporal filtering method based on hypothesis testing is proposed in this paper. A framework of a two-step similarity measure strategy is adopted to further enhance the filtering results. Firstly, bi-date analysis using a two-sample Kolmogorov-Smirnov (KS) test is conducted in step 1 to extract homogeneous patches for 3-D patch stacks generation. Subsequently, the similarity between patch stacks is compared by a sliding time-series likelihood ratio (STSLR) test algorithm in step 2, which utilizes the multi-dimensional data structure of the stacks to improve the accuracy of unchanged pixels detection. Finally, the filtered values are obtained by averaging the similar pixels in time-series. The experimental results and analysis of two multitemporal datasets acquired by TerraSAR-X show that the proposed method outperforms the other typical methods with regard to the overall filtering effect, especially in terms of the consistency between the filtered images and the original ones. Furthermore, the performance of the proposed method is also discussed by analyzing the results from step 1 and step 2.

Anomalies and False Rejections

2020 ◽  
Vol 33 (5) ◽  
pp. 2134-2179 ◽  
Author(s):  
Tarun Chordia ◽  
Amit Goyal ◽  
Alessio Saretto
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Real Data ◽  
Multiple Hypothesis Testing ◽  
Statistical Properties ◽  
Trading Strategies ◽  
Cross Sectional ◽  
Publication Process ◽  
Multiple Hypothesis ◽  
Expected Proportion

Abstract We use information from over 2 million trading strategies randomly generated using real data and from strategies that survive the publication process to infer the statistical properties of the set of strategies that could have been studied by researchers. Using this set, we compute $t$-statistic thresholds that control for multiple hypothesis testing, when searching for anomalies, at 3.8 and 3.4 for time-series and cross-sectional regressions, respectively. We estimate the expected proportion of false rejections that researchers would produce if they failed to account for multiple hypothesis testing to be about 45%.

scholarly journals Hyperoxia enhances slow-wave forebrain states in urethane-anesthetized and naturally sleeping rats

2018 ◽  
Vol 120 (4) ◽  
pp. 1505-1515 ◽  
Author(s):  
Brandon E. Hauer ◽  
Biruk Negash ◽  
Kingsley Chan ◽  
Wesley Vuong ◽  
Frederick Colbourne ◽  
...  
Keyword(s):  
Slow Wave ◽  
Brain Function ◽  
Field Potential ◽  
Blood Levels ◽  
Brain State ◽  
Wave State ◽  
Wave States ◽  
Brain States ◽  
Eeg Recordings ◽  
Spontaneously Breathing

Oxygen (O2) is a crucial element for physiological functioning in mammals. In particular, brain function is critically dependent on a minimum amount of circulating blood levels of O2 and both immediate and lasting neural dysfunction can result following anoxic or hypoxic episodes. Although the effects of deficiencies in O2 levels on the brain have been reasonably well studied, less is known about the influence of elevated levels of O2 (hyperoxia) in inspired gas under atmospheric pressure. This is of importance due to its typical use in surgical anesthesia, in the treatment of stroke and traumatic brain injury, and even in its recreational or alternative therapeutic use. Using local field potential (EEG) recordings in spontaneously breathing urethane-anesthetized and naturally sleeping rats, we characterized the influence of different levels of O2 in inspired gases on brain states. While rats were under urethane anesthesia, administration of 100% O2 elicited a significant and reversible increase in time spent in the deactivated (i.e., slow-wave) state, with concomitant decreases in both heartbeat and respiration rates. Increasing the concentration of carbon dioxide (to 5%) in inspired gas produced the opposite result on EEG states, mainly a decrease in the time spent in the deactivated state. Consistent with this, decreasing concentrations of O2 (to 15%) in inspired gases decreased time spent in the deactivated state. Further confirmation of the hyperoxic effect was found in naturally sleeping animals where it similarly increased time spent in slow-wave (nonrapid eye movement) states. Thus alterations of O2 in inspired air appear to directly affect forebrain EEG states, which has implications for brain function, as well as for the regulation of brain states and levels of forebrain arousal during sleep in both normal and pathological conditions. NEW & NOTEWORTHY We show that alterations of oxygen concentration in inspired air biases forebrain EEG state. Hyperoxia increases the prevalence of slow-wave states. Hypoxia and hypercapnia appear to do the opposite. This suggests that oxidative metabolism is an important stimulant for brain state.

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.

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