An EEG Based Key Generation Cryptosystem using DiffieHellman And AES Electroencephalogram (EEG) has been recently applied in many research fields. However little research has been performed to explore how EEG can be used efficiently in information security and cryptography. This paper aims to highlight the required steps to follow to ensure a strong biometric cryptokey based on EEG signals. To this end, this work presents a new procedure for brain waves extraction, signals analysis before proceeding to the generation and the exchange of a shared cryptographic key to be used to encrypt and decrypt data between two users. In addition, this paper presents the test bed used in our experiments as well as the process of extraction of the required data to obtain a unique and resistant cryptographic key that ensures a secure and robust data exchange. Furthermore, two symmetric cryptographic algorithms are considered to implement our unique biometric sequence. Our experimental results show the effectiveness of our new approach as the results of the error rate tests are positive by proving the uniqueness and security of both algorithms.

Eventrelated potential from EEG for a twostep Identity Authentication System Current problems related to highlevel security access are increasing, leaving organizations and persons unsafe. A recent good candidate to create a robust identity authentication system is based on brain signals recorded with electroencephalograms (EEG). In this paper, EEGbased brain signals of 56 channels, from eventrelated potentials (ERPs), are used for Subject identification. The ERPs are from positive or negative feedbackrelated responses of a P300speller system. The feature extraction part was done with empirical mode decomposition (EMD) extracting 2 intrinsic mode functions (IMFs) per channel, that were selected based on the Minkowski distance. After that, 4 features are computed per IMF; 2 energy features (instantaneous and teager energy) and 2 fractal features (Higuchi and Petrosian fractal dimension). Support vector machine (SVM) was used for the classification stage with an accuracy index computed using 10folds crossvalidation for evaluating the classifierxe2x80x99s performance. Since highdensity EEG information was available, the wellknown backwardelimination and forwardaddition greedy algorithms were used to reduce or increase the number of channels, step by step. Using the proposed method for subject identification from a positive or negative feedbackrelated response and then identify the subject will add a layer to improve the security system. The results obtained show that subject identification is feasible even using a low number of channels: E.g., 0.89 of accuracy using 5 channels with a mixed population and 0.93 with a maleonly population.