Over recent years, some new variants of Permutation entropy have been introduced and applied to EEG analysis, including a conditional variant and variants using some additional metric information or being based on entropies that are 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
Permutation entropy (PE) has been widely exploited to measure the complexity of the electroencephalo...
In this contribution, a comparison between different permutation entropies as classifiers of electro...
Different techniques originated in information theory and tools from nonlinear systems theory have b...
Over recent years, some new variants of Permutation entropy have been introduced and applied to EEG ...
In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the...
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Sp...
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Sp...
There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. ...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
This paper introduces an entropy based method that measures complexity in non-stationary multivariat...
Permutation entropy and order patterns in an EEG signal have been applied by several authors to stud...
In this contribution, a comparison between different permutation entropies as classifiers of electro...
In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-wor...
In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-wor...
Permutation entropy (PE) has been widely exploited to measure the complexity of the electroencephalo...
In this contribution, a comparison between different permutation entropies as classifiers of electro...
Different techniques originated in information theory and tools from nonlinear systems theory have b...
Over recent years, some new variants of Permutation entropy have been introduced and applied to EEG ...
In this paper, we propose a new algorithm to calculate sample entropy of multivariate data. Over the...
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Sp...
Permutation entropy (PeEn) is a complexity measure that originated from dynamical systems theory. Sp...
There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. ...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
Biomedical signals are measurable time series that describe a physiological state of a biological sy...
This paper introduces an entropy based method that measures complexity in non-stationary multivariat...
Permutation entropy and order patterns in an EEG signal have been applied by several authors to stud...
In this contribution, a comparison between different permutation entropies as classifiers of electro...
In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-wor...
In this paper we illustrate the potential of ordinal-patterns-based methods for analysis of real-wor...
Permutation entropy (PE) has been widely exploited to measure the complexity of the electroencephalo...
In this contribution, a comparison between different permutation entropies as classifiers of electro...
Different techniques originated in information theory and tools from nonlinear systems theory have b...
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