scholarly journals Investigating the Effect of Intrinsic Motivation on Alpha Desynchronization Using Sample Entropy

Entropy ◽  
2019 ◽  
Vol 21 (3) ◽  
pp. 237 ◽  
Author(s):  
Tustanah Phukhachee ◽  
Suthathip Maneewongvatana ◽  
Thanate Angsuwatanakul ◽  
Keiji Iramina ◽  
Boonserm Kaewkamnerdpong
Keyword(s):  
Intrinsic Motivation ◽  
Time Series Data ◽  
Brain Activity ◽  
Sample Entropy ◽  
Entropy Method ◽  
Series Data ◽  
Eeg Data ◽  
Smart Education ◽  
Eeg Alpha ◽  
Spectral Perturbation

The effect of motivation and attention could play an important role in providing personalized learning services and improving learners toward smart education. These effects on brain activity could be quantified by EEG and open the path to analyze the efficiency of services during the learning process. Many studies reported the appearance of EEG alpha desynchronization during the attention period, resulting in better cognitive performance. Motivation was also found to be reflected in EEG. This study investigated the effect of intrinsic motivation on the alpha desynchronization pattern in terms of the complexity of time series data. The sample entropy method was used to quantify the complexity of event-related spectral perturbation (ERSP) of EEG data. We found that when participants can remember the stimulus, ERSP was significantly less complex than when they cannot. However, the effect of intrinsic motivation cannot be defined by using sample entropy directly. ERSP’s main effect showed that motivation affects the complexity of ERSP data; longer continuous alpha desynchronization patterns were found when participants were motivated. Therefore, we introduced an algorithm to identify the longest continuous alpha desynchronization pattern. The method allowed us to understand that intrinsic motivation has an effect on recognition at the frontal and left parietal area directly.

scholarly journals Multiscale Sample Entropy of Two-Dimensional Decaying Turbulence

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 245
Author(s):  
Ildoo Kim
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Soap Film ◽  
Sample Entropy ◽  
Series Data ◽  
Entropy Analysis ◽  
Measured Time ◽  
Energetic Analysis ◽  
Turbulence Data ◽  
Decaying Turbulence

Multiscale sample entropy analysis has been developed to quantify the complexity and the predictability of a time series, originally developed for physiological time series. In this study, the analysis was applied to the turbulence data. We measured time series data for the velocity fluctuation, in either the longitudinal or transverse direction, of turbulent soap film flows at various locations. The research was to assess the feasibility of using the entropy analysis to qualitatively characterize turbulence, without using any conventional energetic analysis of turbulence. The study showed that the application of the entropy analysis to the turbulence data is promising. From the analysis, we successfully captured two important features of the turbulent soap films. It is indicated that the turbulence is anisotropic from the directional disparity. In addition, we observed that the most unpredictable time scale increases with the downstream distance, which is an indication of the decaying turbulence.

scholarly journals Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

2018 ◽  
Vol 145 ◽  
pp. 97-104 ◽  
Author(s):  
Ronakben Bhavsar ◽  
Na Helian ◽  
Yi Sun ◽  
Neil Davey ◽  
Tony Steffert ◽  
...  
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Time Series Data ◽  
Sample Entropy ◽  
Series Data ◽  
Time Series Data Analysis

Evaluation of Variation in Surface Solar Irradiance and Clustering of Observation Stations in Japan

2016 ◽  
Vol 55 (10) ◽  
pp. 2165-2180 ◽  
Author(s):  
Takeshi Watanabe ◽  
Takahiro Takamatsu ◽  
Takashi Y. Nakajima
Keyword(s):  
Solar Irradiance ◽  
Time Series Data ◽  
Sample Entropy ◽  
Small Sample ◽  
Series Data ◽  
Observation Data ◽  
Ramp Rate ◽  
New Approach ◽  
Regional Features ◽  
A Value

AbstractVariation in surface solar irradiance is investigated using ground-based observation data. The solar irradiance analyzed in this paper is scaled by the solar irradiance at the top of the atmosphere and is thus dimensionless. Three metrics are used to evaluate the variation in solar irradiance: the mean, standard deviation, and sample entropy. Sample entropy is a value representing the complexity of time series data, but it is not often used for investigation of solar irradiance. In analyses of solar irradiance, sample entropy represents the manner of its fluctuation; large sample entropy corresponds to rapid fluctuation and a high ramp rate, and small sample entropy suggests weak or slow fluctuations. The three metrics are used to cluster 47 ground-based observation stations in Japan into groups with similar features of variation in surface solar irradiance. This new approach clarifies regional features of variation in solar irradiance. The results of this study can be applied to renewable-energy engineering.

scholarly journals Sample Entropy of Human Gait Center of Pressure Displacement: A Systematic Methodological Analysis

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 579 ◽  
Author(s):  
Samira Ahmadi ◽  
Nariman Sepehri ◽  
Christine Wu ◽  
Tony Szturm
Keyword(s):  
Time Series Data ◽  
Inertial Sensors ◽  
Center Of Pressure ◽  
Sampling Rate ◽  
Sample Entropy ◽  
Human Gait ◽  
Series Data ◽  
Low Pass ◽  
Data Points ◽  
Parameter Values

Sample entropy (SampEn) has been used to quantify the regularity or predictability of human gait signals. There are studies on the appropriate use of this measure for inter-stride spatio-temporal gait variables. However, the sensitivity of this measure to preprocessing of the signal and to variant values of template size (m), tolerance size (r), and sampling rate has not been studied when applied to “whole” gait signals. Whole gait signals are the entire time series data obtained from force or inertial sensors. This study systematically investigates the sensitivity of SampEn of the center of pressure displacement in the mediolateral direction (ML COP-D) to variant parameter values and two pre-processing methods. These two methods are filtering the high-frequency components and resampling the signals to have the same average number of data points per stride. The discriminatory ability of SampEn is studied by comparing treadmill walk only (WO) to dual-task (DT) condition. The results suggest that SampEn maintains the directional difference between two walking conditions across variant parameter values, showing a significant increase from WO to DT condition, especially when signals are low-pass filtered. Moreover, when gait speed is different between test conditions, signals should be low-pass filtered and resampled to have the same average number of data points per stride.

scholarly journals Multivariate methods to track the spatiotemporal profile of feature-based attentional selection using EEG

2019 ◽  
Author(s):  
Johannes Jacobus Fahrenfort
Keyword(s):  
Visual Field ◽  
Time Series Data ◽  
Univariate Analysis ◽  
Attentional Selection ◽  
Series Data ◽  
Attentional Processes ◽  
Eeg Data ◽  
Feature Based ◽  
Multivariate Pattern ◽  
The Relationship

This chapter provides a tutorial-style guide to analyzing electroencephalogram (EEG) data contingent on feature-based attentional selection. It is targeted at researchers that currently investigate attentional processes using univariate methods but consider moving to multivariate analyses. The chapter starts by providing examples of classical univariate analysis, in which the EEG signal occurring ipsilateral to the target is subtracted from the signal that occurs in a contralateral electrode (i.e. the classical N2pc, an interhemispheric posterior negativity emerging around 180–200 ms). Next, it shows how the same type of information can also be identified using multivariate pattern analysis (MVPA). MVPA does not restrict one to contrast attentional selection in opposite hemifields, but also allows one to assess attentional selection on the vertical meridian, or even within a quadrant of the visual field, opening up new avenues for research. The chapter demonstrates how to visualize topographic maps of attentional selection when using MVPA, and shows how to assess timing onsets using the percent-amplitude latency method. Finally, it shows how a forward encoding model enables one to characterize the relationship between a continuous experimental variable (such as attended targets positioned on a circle) and EEG activity. This allows one to construct brain patterns for positions in the visual field that were never attended in the data that was used to generate the forward model. This chapter is intended as a practical guide, explaining the methods and providing the scripts that can be used to generate the figures in-line, thus providing a step-by-step cookbook for analyzing neural time series data in the field of feature-based attentional selection.

scholarly journals Evidence for neural rhythms embedded within binocular rivalry

2019 ◽  
Vol 116 (30) ◽  
pp. 14811-14812 ◽  
Author(s):  
Oakyoon Cha ◽  
Randolph Blake
Keyword(s):  
Binocular Rivalry ◽  
Time Course ◽  
Time Series Data ◽  
Brain Activity ◽  
Series Data ◽  
Neural Oscillations ◽  
Large Dataset ◽  
Perceptual Judgments ◽  
Multistable Perception ◽  
Periodic Fluctuations

Evidence for perceptual periodicity emerges from studies showing periodic fluctuations in visual perception and decision making that are accompanied by neural oscillations in brain activity. We have uncovered signs of periodicity in the time course of binocular rivalry, a widely studied form of multistable perception. This was done by analyzing time series data contained in an unusually large dataset of rivalry state durations associated with states of exclusive monocular dominance and states of mixed perception during transitions between exclusive dominance. Identifiable within the varying durations of dynamic mixed perception are rhythmic clusters of durations whose incidence falls within the frequency band associated with oscillations in neural activity accompanying periodicity in perceptual judgments. Endogenous neural oscillations appear to be especially impactful when perception is unusually confounding.

scholarly journals Entropy Analysis of High-Definition Transcranial Electric Stimulation Effects on EEG Dynamics

2019 ◽  
Vol 9 (8) ◽  
pp. 208 ◽  
Author(s):  
Diego C. Nascimento ◽  
Gabriela Depetri ◽  
Luiz H. Stefano ◽  
Osvaldo Anacleto ◽  
Joao P. Leite ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Statistical Tests ◽  
Series Data ◽  
Entropy Analysis ◽  
Summary Statistic ◽  
High Definition ◽  
Stationary Characteristic ◽  
Linear Dynamical Systems ◽  
Eeg Data

A foundation of medical research is time series analysis—the behavior of variables of interest with respect to time. Time series data are often analyzed using the mean, with statistical tests applied to mean differences, and has the assumption that data are stationary. Although widely practiced, this method has limitations. Here we present an alternative statistical approach with sample analysis that provides a summary statistic accounting for the non-stationary nature of time series data. This work discusses the use of entropy as a measurement of the complexity of time series, in the context of Neuroscience, due to the non-stationary characteristic of the data. To elucidate our argument, we conducted entropy analysis on a sample of electroencephalographic (EEG) data from an interventional study using non-invasive electrical brain stimulation. We demonstrated that entropy analysis could identify intervention-related change in EEG data, supporting that entropy can be a useful “summary” statistic in non-linear dynamical systems.

scholarly journals Analysis of EEG Data Using Complex Geometric Structurization

2021 ◽  
pp. 1-28
Author(s):  
E. A. Kwessi ◽  
L. J. Edwards
Keyword(s):  
Time Series ◽  
Dynamical Systems ◽  
Time Series Data ◽  
Health Condition ◽  
Series Data ◽  
Complex Structures ◽  
Embedding Theory ◽  
Novel Approach ◽  
Eeg Data ◽  
Electroencephalogram Eeg

Abstract Electroencephalogram (EEG) is a common tool used to understand brain activities. The data are typically obtained by placing electrodes at the surface of the scalp and recording the oscillations of currents passing through the electrodes. These oscillations can sometimes lead to various interpretations, depending on, for example, the subject's health condition, the experiment carried out, the sensitivity of the tools used, or human manipulations. The data obtained over time can be considered a time series. There is evidence in the literature that epilepsy EEG data may be chaotic. Either way, the Embedding Theory in dynamical systems suggests that time series from a complex system could be used to reconstruct its phase space under proper conditions. In this letter, we propose an analysis of epilepsy EEG time series data based on a novel approach dubbed complex geometric structurization. Complex geometric structurization stems from the construction of strange attractors using Embedding Theory from dynamical systems. The complex geometric structures are themselves obtained using a geometry tool, the α-shapes from shape analysis. Initial analyses show a proof of concept in that these complex structures capture the expected changes brain in lobes under consideration. Further, a deeper analysis suggests that these complex structures can be used as biomarkers for seizure changes.

scholarly journals EEG-riculture: Sustainability and Butterfly-Effects

2020 ◽  
Vol 1 (1) ◽  
Keyword(s):  
Time Series ◽  
Cognitive Science ◽  
Time Series Data ◽  
Series Data ◽  
Similarity Analysis ◽  
Data Series ◽  
Human Beings ◽  
Eeg Data ◽  
Science Approach ◽  
Butterfly Effect

The motivational background of this paper is to shed new light on the phenomena of butterfly effect and sustainability from a scientific-philosophical and mathematical point of view. We aim to reveal the connection between butterfly effect and sustainability by observing the observer him- or herself and exploring the most significant errors of thinking and operation of the subject, while analyzing the peculiarities of the butterfly effect. Our reasoning is based on cognitive science approach, agricultural scientific experiments, and on parallel EEG (electroencephalogram) measurements. The latter, emerged from the research area of Innoria’s Team Flow Research Team, is a completely new methodological approach in the field of cognitive science on the basis of previous comparative behavioral scientific results1, but built up on new technological opportunities and professional standpoint2,3. As a result, we can see a new contexts and define problems in measurement methodology, while researching the interactions of human minds. These EEG measurements are part of an extensive research, which focuses on the identification of the parallel perception of reality and the synchronized perception-reaction relation of human beings. In the philosophy of science approach the butterfly effect is always provided by the observer by using in his/her rationing the indicator 'small' or 'seemingly insignificant', while one finds that the effect is not linearly related to such approximate (quantitative) attributes of the cause. The consequence is unexpectedly, unpredictably large, as compared to the observer's expectations. Therefore, the problem requires a change of perspective, namely, one needs to confer much greater importance to small causes. To discover these causes, we need to explore the mechanism of human observation much more intensively. The mathematical objective of the paper is to demonstrate an explored butterfly-effect process, based on a real, but anonymous parallel measured EEG data asset, where each step is reproducible. The problems that need to be solved are: (i) How can we classify correctly over EEG measurements the personal time series data (raw individual EEG data series with 0.25 second sampling) within the frame of similarity analysis? (ii) How to deal with the butterfly effect? (iii) How to step forward on the theoretical path of chaotic systems4 designated by Edward N. Lorenz? The butterfly-effect is the unexpected difference between the result of a classification based on a given data asset and the result of another classification, based on a data asset, having just one additional record as the input; in this case, we have data at about every 0.25 s, where the used length of the time series can be over 100 or 1000. Differences will be derived by means of ranked inputs – especially in case of data having the same value. Similarity analysis is a typical ranking-oriented modelling scheme, where these special effects can be detected at once, without the need for any further manipulations. Since similarity analysis produces model chains, symmetry-driven similarity analyses can have, as well, butterfly-effects in a consistence-oriented model structure. Sustainability can be regarded as a mathematical issue5, being a dynamic phenomenon. Sustainability may be redefined as a capability of forecasting system behavior. Random-like, not-planned incidents cannot be accepted as sustainable and realized plan values. The most trivial usage of the ‘here and now’ characterized sustainability approach is precision farming and its analogy, the EEG-riculture, as such.

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