ArticleEntropy (Basel, Switzerland)2021
A Modified Multivariable Complexity Measure Algorithm and Its Application for Identifying Mental Arithmetic Task.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.Entropy (Basel, Switzerland) · 2024Article
- A discrete Huber-Braun neuron model: from nodal properties to network performance.Cognitive neurodynamics · 2023Article
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3 authors.
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Abstract
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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