True Random Number Generation from Bioelectrical and Physical Signals

It is possible to generate personally identifable random numbers to be used in some particular applications, such as authentication and key generation. Tis study presents the true random number generation from bioelectrical signals like EEG, EMG, and EOG and physical signals, such as blood volume pulse, GSR (Galvanic Skin Response), and respiration. Te signals used in the random number generation were taken from BNCIHORIZON2020 databases. Random number generation was performed from ffeen diferent signals (four from EEG, EMG, and EOG and one from respiration, GSR, and blood volume pulse datasets). For this purpose, each signal was frst normalized and then sampled. Te sampling was achieved by using a nonperiodic and chaotic logistic map.Ten, XOR postprocessing was applied to improve the statistical properties of the sampled numbers. NIST SP 800-22 was used to observe the statistical properties of the numbers obtained, the scale index was used to determine the degree of nonperiodicity, and the autocorrelation tests were used to monitor the 0-1 variation of numbers. Te numbers produced from bioelectrical and physical signals were successful in all tests. As a result, it has been shown that it is possible to generate personally identifable real random numbers from both bioelectrical and physical signals.