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

Network security is very important when sending confidential data through the network. Cryptography is the science of hiding information, and a combination of cryptography solutions with cognitive science starts a new branch called cognitive cryptography that guarantee the confidentiality and integrity of the data. Brain signals as a biometric indicator can convert to a binary code which can be used as a cryptographic key. This paper proposes a new method for decreasing the error of EEG based key generation process. Discrete Fourier Transform, Discrete Wavelet Transform, Autoregressive Modeling, Energy Entropy, and Sample Entropy were used to extract features. All features are used as the input of new method based on window segmentation protocol then are converted to the binary mode. We obtain 0.76%, and 0.48% mean Half Total Error Rate (HTER) for 18-channel and single-channel cryptographic key generation systems respectively.