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Updated: Jul 23, 2026
11:15
Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
Published on: June 27, 2013
Effective normalization of complexity measurements for epoch length and sampling frequency.
P E Rapp1, C J Cellucci, K E Korslund
1Department of Pharmacology and Physiology, Medical College of Pennsylvania Hahnemann University, Philadelphia, Pennsylvania 19129, USA. Paul.E.Rapp@Drexel.edu
Summary
Algorithmic redundancy offers a robust measure of information, unaffected by message length or sampling frequency. This contrasts with algorithmic complexity, providing a more stable analysis for stationary systems.
Area of Science:
- Information Theory
- Computational Complexity
- Signal Processing
Background:
- Algorithmic complexity is sensitive to message length and sampling frequency in symbolic sequences.
- Shannon's information redundancy is a foundational concept but has limitations in certain applications.
Purpose of the Study:
- To introduce sequence-sensitive generalizations of information redundancy.
- To demonstrate algorithmic redundancy's insensitivity to message length and sampling frequency for stationary systems.
Main Methods:
- Developing novel definitions for algorithmic redundancy.
- Comparing algorithmic redundancy with algorithmic complexity using stationary systems.
Main Results:
- Algorithmic redundancy is demonstrated to be insensitive to message length.
- Algorithmic redundancy is shown to be insensitive to observation scale (sampling frequency) in stationary systems.
- This insensitivity offers advantages over algorithmic complexity.
Conclusions:
- Algorithmic redundancy provides a stable measure of information for stationary systems.
- The developed definitions overcome limitations of algorithmic complexity regarding sequence length and sampling rate.
- This work advances the understanding and application of information redundancy in signal analysis.
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