scholarly journals Permutation Entropy: New Ideas and Challenges

2017 ◽  
Author(s):  
Karsten Keller ◽  
Teresa Mangold ◽  
Inga Stolz ◽  
Jenna Werner
Keyword(s):  
Shannon Entropy ◽  
Permutation Entropy ◽  
Eeg Analysis ◽  
New Developments ◽  
Eeg Data ◽  
New Ideas ◽  
Metric Information

During the last years some new variants of Permutation entropy have been introduced and applied to EEG analysis, among them a conditional variant and variants using some additional metric information or being based on entropies different from the Shannon entropy. In some situations it is not completely clear what kind of information the new measures and their algorithmic implementations provide. We discuss the new developments and illustrate them for EEG data.

Awash in the Storm of National Revolution

2016 ◽  
Author(s):  
Motoe Sasaki
Keyword(s):  
United States ◽  
Gender Differences ◽  
The United States ◽  
Women Missionaries ◽  
New Developments ◽  
New Women ◽  
New Ideas

This chapter explores the aftermath of the collapse of the Wilsonian moment and its uneven and gendered effects on American New Women missionaries' enterprises in the Nationalist Revolution period (1924–27). It was at this time that the missionaries came to feel the power of the national revolution movement and found their projects were being reframed within new ideas and articulated in a new vocabulary that had become current in China. In taking such changes into account, they had to interpret and respond to new developments and ultimately reconsider their own perceptions of the United States and the very nature of their existence in China. Local Chinese resistance to their educational projects and institutions directed toward American New Women missionaries also brought into play gender differences and issues among the Chinese themselves and consequently made the difficulties facing the missionaries all the more complex and entrenched.

Expressive Minds and Artistic Creations

2018 ◽  
Keyword(s):  
Conceptual Blending ◽  
Cognitive Mechanisms ◽  
Multiple Perspectives ◽  
Cognitive Poetics ◽  
Research Papers ◽  
Multimodal Texts ◽  
New Developments ◽  
New Ideas ◽  
Research Findings

Expressive Minds and Artistic Creations: Studies in Cognitive Poetics presents multidisciplinary and interdisciplinary research papers describing new developments in the field of cognitive poetics. The chapters examine the complex connections between cognition and poetics with special attention given to how people both create and interpret novel artistic works in a variety of expressive media, including literature, music, art, and multimodal artifacts. The authors have diverse disciplinary backgrounds, but all of them embrace theories and research findings from multiple perspectives, such as linguistics, psychology, literary studies, music, art, neuroscience, and media studies. Several authors explicitly discuss empirical and theoretical challenges in doing interdisciplinary work, which many believe is essential to future progress in cognitive poetics. Scholars address many specific research questions in their chapters, such as, most notably, the role of embodiment and simulation in human imagination, the importance of conceptual metaphors and conceptual blending processes in the creation and interpretation of literature, and the function of multiperspectivity in poetic and multimodal texts. Several new ideas are also advanced in the volume regarding the cognitive mechanisms responsible for artistic creations and understandings. The volume overall offers an expanded view of cognitive poetics research that situates the study of expressive minds within a broader range of personal, social, cultural, and historical contexts. Among other leading researchers, contributors include world-famous scholars of psychology, linguistics, and literature—Raymond W. Gibbs, Jr., Zoltásn Kövecses, and Reuven Tsur—whose defining papers also survey the roles and significance of conceptual mechanisms in literature.

Experimental Identification of Model Parameters and the Statistical Processing Using a Nonlinear Oscillator Applied to EEG Analysis

2018 ◽  
Author(s):  
Kenyu Uehara ◽  
Takashi Saito
Keyword(s):  
Stress State ◽  
Nonlinear Oscillator ◽  
Statistical Processing ◽  
Psychological State ◽  
Model Parameters ◽  
Eeg Analysis ◽  
Analysis Model ◽  
Eeg Data ◽  
Machine Interface ◽  
Almost All

The nonlinear analysis may help to reveal the complex behavior of the Electroencephalogram (EEG) signal. In order to analyze the EEG in real time, we have proposed an EEG analysis model using a nonlinear oscillator with one degree of freedom and minimum required parameters. Our method identifies EEG model parameters experimentally. The purpose of this study is to examine the specific characteristic of model parameters. Validation of the method and investigation of characteristic of model parameters were conducted based on alpha frequency EEG data in both relax state and stress state. The results of the parameter identification with the time sliding window for 1 second show almost all of the identified parameters have a normal distribution spread around the average. The model outputs can closely match the complicated experimental EEG data. The results also showed that the existence of nonlinear term in the EEG analysis is crucial and the linearity parameter shows a certain tendency as the nonlinearity increases. Furthermore, the activities of EEG become linear on the mathematical model when suddenly change from the relax state to the stress state. The results indicate that our method may provide useful information in various field including the quantification of human mental or psychological state, diagnosis of brain disease such as epilepsy and design of brain machine interface.

EEG analysis periods of minimum length for detection of consciousness and unconsciousness by approximate and permutation entropy

2011 ◽  
Vol 28 ◽  
pp. 31
Author(s):  
S. Pilge ◽  
D. Jordan ◽  
S. Paprotny ◽  
M. Kreuzer ◽  
E. F. Kochs ◽  
...  
Keyword(s):  
Permutation Entropy ◽  
Minimum Length ◽  
Eeg Analysis

scholarly journals A Novel Local Explainability Approach for Spectral Insight into Raw EEG-Based Deep Learning Classifiers

2021 ◽  
Author(s):  
Charles A Ellis ◽  
Robyn L Miller ◽  
Vince Calhoun
Keyword(s):  
Deep Learning ◽  
Sleep Stage ◽  
Sleep Stages ◽  
Eeg Analysis ◽  
Frequency Bands ◽  
Learning Classifiers ◽  
Eeg Data ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Insight Into

The frequency domain of electroencephalography (EEG) data has developed as a particularly important area of EEG analysis. EEG spectra have been analyzed with explainable machine learning and deep learning methods. However, as deep learning has developed, most studies use raw EEG data, which is not well-suited for traditional explainability methods. Several studies have introduced methods for spectral insight into classifiers trained on raw EEG data. These studies have provided global insight into the frequency bands that are generally important to a classifier but do not provide local insight into the frequency bands important for the classification of individual samples. This local explainability could be particularly helpful for EEG analysis domains like sleep stage classification that feature multiple evolving states. We present a novel local spectral explainability approach and use it to explain a convolutional neural network trained for automated sleep stage classification. We use our approach to show how the relative importance of different frequency bands varies over time and even within the same sleep stages. Furthermore, to better understand how our approach compares to existing methods, we compare a global estimate of spectral importance generated from our local results with an existing global spectral importance approach. We find that the δ band is most important for most sleep stages, though β is most important for the non-rapid eye movement 2 (NREM2) sleep stage. Additionally, θ is particularly important for identifying Awake and NREM1 samples. Our study represents the first approach developed for local spectral insight into deep learning classifiers trained on raw EEG time series.

Novel Nonlinear Approach for Real-Time Fatigue EEG Data: An Infinitely Warped Model of Weighted Permutation Entropy

2020 ◽  
Vol 21 (6) ◽  
pp. 2437-2448
Author(s):  
Edmond Q. Wu ◽  
Li-Min Zhu ◽  
Wen-Ming Zhang ◽  
Ping-Yu Deng ◽  
Bo Jia ◽  
...  
Keyword(s):  
Real Time ◽  
Permutation Entropy ◽  
Nonlinear Approach ◽  
Eeg Data

Special symposium: The upcoming 90th anniversary in 2019 of Berger’s discovery of EEG-4. New developments in quantitative EEG analysis

2019 ◽  
Vol 130 (10) ◽  
pp. e175
Author(s):  
Masafumi Yoshimura ◽  
Keiichiro Nishida ◽  
Yuichi Kitaura ◽  
Shunichiro Ikeda ◽  
Roberto D. Pascual-Marqui ◽  
...  
Keyword(s):  
Quantitative Eeg ◽  
Eeg Analysis ◽  
New Developments ◽  
Quantitative Eeg Analysis ◽  
90Th Anniversary ◽  
Special Symposium

ACCURATE DETECTION OF SEIZURE USING NONLINEAR PARAMETERS EXTRACTED FROM EEG SIGNALS

2019 ◽  
Vol 19 (01) ◽  
pp. 1940004 ◽  
Author(s):  
JAHMUNAH VICNESH ◽  
YUKI HAGIWARA
Keyword(s):  
Kolmogorov Complexity ◽  
Brain Activity ◽  
Mean Value ◽  
Permutation Entropy ◽  
Cad System ◽  
Eeg Data ◽  
Nonlinear Features ◽  
Best Parameter ◽  
Value Decomposition

Electroencephalography (EEG) is the graphical recording of electrical activity along the scalp. The EEG signal monitors brain activity noninvasively with a high accuracy of milliseconds and provides valuable discernment about the brain’s state. It is also sensitive in detecting spikes in epilepsy. Computer-aided diagnosis (CAD) tools allow epilepsy to be diagnosed by evading invasive methods. This paper presents a novel CAD system for epilepsy using other linear features together with Hjorth’s nonlinear features such as mobility, complexity, activity and Kolmogorov complexity. The proposed method uses MATLAB software to extract the nonlinear features from the EEG data. The optimal features are selected using the statistical analysis, ANOVA (analysis of variance) test for classification. Once selected, they are fed into the decision tree (DT) for the classification of the different epileptic classes. The proposed method affirms that four nonlinear features, Kolmogorov complexity, singular value decomposition, mobility and permutation entropy are sufficient to provide the highest accuracy of 93%, sensitivity of 97%, specificity of 88% and positive predictive value (PPV) of 94%, with the DT classifier. The mean value is the highest in the ictal stage for the Kolmogorov complexity proving it to have the best variation. It also has the highest [Formula: see text]-value of 300.439 portraying it to be the best parameter that is favourable for the clinical diagnosis of epilepsy, when used together with the DT classifier, for a duration of 23.6[Formula: see text]s of EEG data.

EEG Analysis of Imagined Speech

2020 ◽  
pp. 679-692
Author(s):  
Sadaf Iqbal ◽  
Muhammed Shanir P.P. ◽  
Yusuf Uzzaman Khan ◽  
Omar Farooq
Keyword(s):  
Mean Absolute Deviation ◽  
Eeg Analysis ◽  
Absolute Deviation ◽  
Scalp Eeg ◽  
Computer Interfaces ◽  
Eeg Data ◽  
Scalp Electroencephalogram ◽  
Electroencephalogram Eeg ◽  
Control State ◽  
Excellent Tool

Scalp electroencephalogram (EEG) is one of the most commonly used methods to acquire EEG data for brain-computer interfaces (BCIs). Worldwide a large number of people suffer from disabilities which impair normal communication. Communication BCIs are an excellent tool which helps the affected patients communicate with others. In this paper scalp EEG data is analysed to discriminate between the imagined vowel sounds /a/, /u/ and no action or rest as control state. Mean absolute deviation (MAD) and Arithmetic mean are used as features to classify data into one of the classes /a/, /u/ or rest. With high classification accuracies of 87.5-100% for two class problem and 78.33-96.67% for three class problem that have been obtained in this work, this algorithm can be used in communication BCIs, to develop speech prosthesis and in synthetic telepathy systems.

A Case Study on EEG Analysis: Embedding Entropy Estimations Indicate the Decreased Neuro-Cortical Complexity Levels Mediated by Methylphenidate Treatment in Children With ADHD

2021 ◽  
pp. 155005942110640
Author(s):  
Fatih Hilmi Çetin ◽  
Miraç Barış Usta ◽  
Serap Aydın ◽  
Ahmet Sami Güven
Keyword(s):  
Complexity Analysis ◽  
Approximate Entropy ◽  
Quantitative Eeg ◽  
Symptom Cluster ◽  
Permutation Entropy ◽  
Eeg Analysis ◽  
Pre Treatment ◽  
Cortical Regions ◽  
The Impact ◽  
The Brain

Objective: Complexity analysis is a method employed to understand the activity of the brain. The effect of methylphenidate (MPH) treatment on neuro-cortical complexity changes is still unknown. This study aimed to reveal how MPH treatment affects the brain complexity of children with attention deficit hyperactivity disorder (ADHD) using entropy-based quantitative EEG analysis. Three embedding entropy approaches were applied to short segments of both pre- and post- medication EEG series. EEG signals were recorded for 25 boys with combined type ADHD prior to the administration of MPH and at the end of the first month of the treatment. Results: In comparison to Approximate Entropy (ApEn) and Sample Entropy (SampEn), Permutation Entropy (PermEn) provided the most sensitive estimations in investigating the impact of MPH treatment. In detail, the considerable decrease in EEG complexity levels were observed at six cortical regions (F3, F4, P4, T3, T6, O2) with statistically significant level ( p < .05). As well, PermEn provided the most meaningful associations at central lobes as follows: 1) The largeness of EEG complexity levels was moderately related to the severity of ADHD symptom detected at pre-treatment stage. 2) The percentage change in the severity of opposition as the symptom cluster was moderately reduced by the change in entropy. Conclusion: A significant decrease in entropy levels in the frontal region was detected in boys with combined type ADHD undergoing MPH treatment at resting-state mode. The changes in entropy correlated with pre-treatment general symptom severity of ADHD and conduct disorder symptom cluster severity.

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