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Probing Intrinsic Neural Timescales in EEG with an Information-Theory Inspired Approach: Permutation Entropy Time
Andrea Buccellato1,2, Yasir Çatal3, Patrizia Bisiacchi1,2
1Padova Neuroscience Center, University of Padova, Via Orus 2/B, 35129 Padova, Italy.
Entropy (Basel, Switzerland)
|July 29, 2023
Summary
Permutation entropy (PE) offers a robust method for estimating time delays in neural data, outperforming traditional autocorrelation window methods. This new approach, permutation entropy-time delay estimation (PE-TD), accurately measures intrinsic neural timescales and shows promise for characterizing consciousness states.
Area of Science:
- Neuroscience
- Complex Systems
- Information Theory
Background:
- Time delays are crucial in physical systems, including the brain, influencing dynamics and playing a role in consciousness.
- Estimating these time delays from neural time series is essential but often challenging.
- Current methods like the autocorrelation window (ACW) have limitations, especially with nonstationary data.
Purpose of the Study:
- To introduce and validate permutation entropy (PE) as a novel method for estimating time delays in neural time series.
- To compare the efficacy of PE-based time delay estimation (PE-TD) against the traditional ACW method.
- To explore the application of PE-TD in measuring intrinsic neural timescales (INTs) and its potential in characterizing disorders of consciousness (DoCs).
Main Methods:
- Utilized permutation entropy (PE) to estimate time delays from both synthetic and human hd-EEG neural data.
- Validated the PE-TD method on synthetic data, assessing its robustness against nonstationarity.
- Measured intrinsic neural timescales (INTs) using PE-TD in healthy individuals and patients with disorders of consciousness (DoCs).
Main Results:
- Demonstrated the validity and robustness of PE-TD for estimating time delays in neural time series, even under nonstationary conditions.
- Successfully measured intrinsic neural timescales (INTs) from human hd-EEG data using PE-TD.
- Observed a state-dependent decrease in correlation between ACW and PE-TD in disorders of consciousness, suggesting different neural dynamics in conscious versus unconscious states.
Conclusions:
- PE-TD is a valid and robust tool for extracting relevant timescales from neural data.
- The divergence between ACW and PE-TD in DoC subjects highlights PE-TD's potential for characterizing conscious states.
- This method offers a promising alternative for analyzing neural dynamics and understanding consciousness.
Keywords:
consciousnesselectroencephalographyintrinsic neural timescalesneural time delaypermutation entropy
