Development of a Unique Biometric-based Cryptographic Key Generation with Repeatability using Brain Signals

Network security is very important when confidential data is sent through a network. Cryptography is the science of hiding information, and a combination of cryptography solutions and cognitive science starts a new branch called cognitive cryptography that guarantees the confidentiality and integrity of the data. Brain signals, as a biometric indicator, can be converted to a binary code, which can be used as a cryptographic key. In this paper, we propose a new method for decreasing the error of the electroencephalogram-based key generation process. Discrete Fourier transform, discrete wavelet transform, autoregressive modeling, energy entropy, and sample entropy are used to extract the features. All features are used as the input of the new method based on the window segmentation protocol, and then are converted to the binary mode. We obtained the 0.76% and 0.48% mean half total error rate (HTER) for the 18-channel and single-channel cryptographic key generation systems, respectively.