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Kendall transfer entropy: a novel measure for estimating information transfer in complex systems
Xin Wen1,2, Zhenhu Liang1,2, Jing Wang3
1Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, People's Republic of China.
Journal of Neural Engineering
|July 10, 2023
Summary
We introduce Kendall Transfer Entropy (KTE), a new method enhancing causal inference in neuroscience. KTE excels with small sample sizes, outperforming existing techniques in accuracy and robustness for analyzing neural signals.
Area of Science:
- Neuroscience
- Information Theory
- Dynamical Systems Analysis
Background:
- Transfer entropy (TE) is crucial for inferring causality in dynamical systems, particularly in neuroscience.
- Estimating TE often faces limitations due to small sample sizes in neural signal analysis.
- Kendall transformation offers advantages for information-theoretic measures with limited data.
Purpose of the Study:
- To introduce Kendall correlation into TE estimation, creating Kendall TE (KTE).
- To evaluate KTE's effectiveness compared to existing TE estimation methods.
- To assess KTE's performance with small sample sizes and noisy data.
Main Methods:
- Developed Kendall TE (KTE) by integrating Kendall transformation with TE estimation.
- Compared KTE against adaptive partitioning (D-V partitioning) and symbolic TE.
- Validated methods using simulations (linear, nonlinear, neural mass models) and real EEG data.
Main Results:
- KTE demonstrated superior performance in identifying coupling direction with small sample sizes.
- KTE showed enhanced sensitivity to coupling strength and noise resistance.
- KTE accurately detected altered frontal-parietal connectivity during propofol-induced anesthesia in EEG.
Conclusions:
- Kendall TE (KTE) is a robust and powerful tool for estimating transfer entropy, especially with limited data.
- KTE offers an innovative approach to quantizing time series for information-theoretic analysis.
- The method has significant potential for advancing causal inference in neuroscience and other fields.
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