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Cortical Source Analysis of High-Density EEG Recordings in Children

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Chaotic Visual Cryptosystem Using Empirical Mode Decomposition Algorithm for Clinical EEG Signals.

Chin-Feng Lin¹

¹Department of Electrical Engineering, National Taiwan Ocean University, Taiwan, Republic of China. lcf1024@mail.ntou.edu.tw.

Journal of Medical Systems

|December 10, 2015

Summary

This summary is machine-generated.

This study introduces a novel chaotic visual cryptosystem for electroencephalography (EEG) signals using Empirical Mode Decomposition (EMD). The method offers robust and secure encryption for sensitive clinical EEG data.

Keywords:

Chaotic visual cryptosystem Clinical EEG EMD Encryption parameters Energy-IMF distribution feature

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Area of Science:

Neuroscience
Computer Science
Cryptography

Background:

Clinical electroencephalography (EEG) signals contain sensitive patient data requiring secure transmission and storage.
Existing encryption methods may not adequately address the unique characteristics of biological signals like EEG.
The need for robust and unpredictable visual encryption mechanisms for medical data is paramount.

Purpose of the Study:

To propose a novel chaotic visual cryptosystem for clinical EEG signals.
To integrate Empirical Mode Decomposition (EMD) with chaos-based encryption for enhanced security.
To develop a robust and unpredictable encryption mechanism for EEG data.

Main Methods:

Integration of two-dimensional (2D) chaos-based encryption scramblers, the EMD algorithm, and a 2D block interleaver.
Utilizing energy-intrinsic mode function (IMF) distribution features of EEG signals for chaotic encryption parameters.
Employing logistic map-based chaotic signal generation using energy ratios of IMFs for starting points and security levels.

Main Results:

Tested on three EEG databases and seventeen clinical EEG signals.
Achieved excellent encryption effects with average r and mse values of 0.0201 and 4.2626 × 10(-29) respectively.
Demonstrated high security, as chaotically-encrypted signals cannot be recovered with minute parameter errors (e.g., 0.000001% initial point error).

Conclusions:

The proposed chaotic EMD visual encryption mechanism provides excellent encryption effects for clinical EEG signals.
The integration of EMD and chaotic systems offers a robust solution for securing sensitive medical data.
The system's sensitivity to input parameters ensures a high level of security and unpredictability.