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A data security scheme based on EEG characteristics for body area networks.

Tong Bai1, Yuhao Jiang1, Jiazhang Yang2

  • 1School of Optoelectronic Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China.

Frontiers in Neuroscience
|June 5, 2023
PubMed
Summary

This study introduces a novel data encryption method for Body Area Networks (BANs) using electroencephalogram (EEG) signals and a linear feedback shift register (LFSR). The technique enhances security for telemedicine by ensuring high randomness and low complexity in data transmission.

Keywords:
body area networkdata securityelectroencephalogramlinear feedback shift registerwavelet packet transform

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Area of Science:

  • Biomedical Engineering
  • Cybersecurity
  • Signal Processing

Background:

  • Body Area Networks (BANs) are crucial for telemedicine, enabling wireless wearable devices.
  • Security of BAN data is a significant challenge hindering widespread adoption in healthcare.
  • Existing security measures often fall short in practical, real-world telemedicine applications.

Purpose of the Study:

  • To propose a robust data encryption method for Body Area Networks (BANs).
  • To address the critical issue of data security in telemedicine applications.
  • To enhance the reliability and trustworthiness of wireless wearable device networks.

Main Methods:

  • Extracted electroencephalogram (EEG) signal characteristics using wavelet packet transform.
  • Utilized Message-Digest Algorithm 5 (MD5) with EEG features for random key generation.
  • Employed a linear feedback shift register (LFSR) to generate stream keys for data encryption.

Main Results:

  • Achieved a very low correlation coefficient between data before and after encryption.
  • Demonstrated that attackers face significant difficulty in extracting statistical features of the plaintext.
  • Validated the effectiveness of the proposed EEG-based security scheme through experimental evaluations.

Conclusions:

  • The proposed EEG-based encryption method offers high randomness for BAN data security.
  • The scheme exhibits low computational complexity, making it suitable for resource-constrained wearable devices.
  • This approach presents a promising solution for securing sensitive data in telemedicine and BAN systems.