A chaos-based visual encryption mechanism for clinical EEG signals.

In this study, we have developed a chaos-based visual encryption mechanism that can be applied for clinical electroencephalography (EEG) signals. In comparison with other types of random sequences, chaos sequences were mainly used to increase unpredictability. We used a 1D chaotic scrambler and a permutation scheme to achieve EEG visual encryption. One approach of realizing the visual encryption mechanism is to scramble the signal values of the input EEG signal by multiplying a 1D chaotic signal to randomize the EEG signal values. We then applied a chaotic address scanning order encryption to the randomized reference values. Simulation results show that when the correct deciphering parameters are entered, the signal is completely recovered, and the percent root-mean-square difference (PRD) values for control and alcoholic clinical EEG signals are 4.33 x 10(-15) and 4.11 x 10(-15)%, respectively. As long as there is an input parameter error, with an initial point error of 0.00000001% as an example, thereby making these clinical EEG signals unrecoverable.