A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task

A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task

Properly measuring the complexity of time series is an important issue. The permutation entropy (PE) is a widely used as an effective complexity measurement algorithm, but it is not suitable for the complexity description of multi-dimensional data. In this paper, in order to better measure the compl...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 23; no. 8; p. 931
Main Authors: Ma, Dizhen, He, Shaobo, Sun, Kehui
Format: Journal Article
Language:English
Published: Basel MDPI AG 22-07-2021
MDPI
Subjects:
Algorithms
Arithmetic
chaotic series
Complexity
EEG signal
Entropy
Methods
Multidimensional data
multivariable
Noise
PCA
permutation entropy
Permutations
Principal components analysis
Reduction
Researchers
Signal processing
System effectiveness
Time measurement
Time series
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Summary:Properly measuring the complexity of time series is an important issue. The permutation entropy (PE) is a widely used as an effective complexity measurement algorithm, but it is not suitable for the complexity description of multi-dimensional data. In this paper, in order to better measure the complexity of multi-dimensional time series, we proposed a modified multivariable PE (MMPE) algorithm with principal component analysis (PCA) dimensionality reduction, which is a new multi-dimensional time series complexity measurement algorithm. The analysis results of different chaotic systems verify that MMPE is effective. Moreover, we applied it to the comlexity analysis of EEG data. It shows that the person during mental arithmetic task has higher complexity comparing with the state before mental arithmetic task. In addition, we also discussed the necessity of the PCA dimensionality reduction.
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content type line 23
ISSN:1099-4300
1099-4300
DOI:10.3390/e23080931