AUTHOR=Bai Tong , Jiang Yuhao , Yang Jiazhang , Luo Jiasai , Du Ya 

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

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1174096

DOI=10.3389/fnins.2023.1174096

ISSN=1662-453X

ABSTRACT=Body area network (BAN) is a body-centered network of wireless wearable devices. As the basic technology of telemedicine service, BAN has aroused intense interest in academia and industry, and provides a new technical to solve the problems existing in medical filed. However, how to guarantee security has become a technical problem that hinders the further development of BAN technology. In this paper, we propose a data encryption method based on electroencephalogram (EEG) characteristic values and LSFR to solve the problem of data security in BAN. First, the characteristics of human EEG signals are extracted based on the wavelet packet transform method, and as the MD5 input data to ensure the its randomness. Then, a linear feedback shift register (LFSR) stream key generator is adopted. The 128-bit initial key obtained through message digest algorithm 5 (MD5) is used to generate the key stream for BAN data encryption. Finally, the effectiveness of the proposed security scheme is verified by various experimental evaluations. The experiment results show that correlation coefficient of data before and after encryption is very low, and it is difficult for the attacker to obtain the statistical features of the plaintext. Therefore, the EEG-based security scheme proposed in this paper has the advantages of high randomness and low computational complexity for BAN systems.