A Study on Combining EEG Signals and Crytography for Bitcoin Security

In this paper, we propose two EEG-based systems including cryptographic key generation and (true) random number generation to enhance Bitcoin security. The first system is based on the quasi-stationary characteristic of EEG signals when analyzed in a sufficient short time window. With this quasi-stationary, stable EEG feaures are extracted and corrected to generate cryptographic keys from EEG-based authentication for the protection of Bitcoin wallets. The second one is based on the non-linear and chaotic characteristics of EEG signals. By mathematical transformation, EEG signals can be transformed to be random binary sequences for the use of protecting digital signatures in Bitcoin transactions. Two EEG datasets which are DEAP and GrazIIIa were used to validate the performance of the proposed system. Our experimental results showed that both cryptographic keys and random numbers are securely derived with very high success rates and very high rates of passing the standard statistical tests recommended by the National Institute of Standard and Technology (NIST) for examining the quality of randomness, especially in cryptography applications.