## 2 State of the Art
At the testing stage, the subjects were shown the same images in random order, but when a \"black label\" appeared before the image, the subjects presented the image password. The system identified the subjects without errors even 514 days after training. The disadvantage of this method is the lengthy training procedure, as well as the need to create ideal conditions for use, imposing significant restrictions on the application of the method in real practice. The authors of the work [16] claim that using the spectral power of brain gamma waves as features increases the accuracy of biometric authentication by EEG. An experiment in which EEG data from 109 subjects from the PhysioNet database were used (subjects were in a calm state with open eyes and closed eyes) was conducted. The obtained recognition reliability estimate was EER = 0.0196. Multifactorial identification systems are known that use EEG and eye blink data [17]. The accuracy of the two-factor procedure for subject recognition has been obtained from 92.4% to 97.6%. In [18], wavelet analysis and deconvolutional neural network (DNN) were used to recognize EEG images; as a result, a subject recognition accuracy of 94% was achieved. The issues of creating models for generating cryptographic keys or passwords based on EEG data, which are subsequently used for authentication, are covered quite widely in the literature. Various authors consider the possibility of using schemes based on fuzzy extractor, fuzzy vault, and fuzzy commitment. In particular, the work [19] presents the results of an experiment on assessing the reliability of key generation involving 42 subjects (the probability of errors was 0.024). Some authors have assessed the influence of a person's emotional state on the stability of key generation using EEG data [20]. The subjects' emotional state was changed by various influences. The results showed that the EEG features used depend to a significant extent on the subject's state, which affects the reliability of key generation (the probability of error increases). In [21], an EEG-based identification method using code-modulated visual-evolved potentials (c-VEPs) was proposed. The authors recorded two data sessions for each individual on different days to measure the performance of intra-session and cross-session identification. State-of-the-art VEP detection algorithms in brain-computer interfaces (BCIs) were used to build the template-matching-based identification framework. For cross-session identification, the error rate was 99.43% using 10.5-second EEG signals. It is evident from the presented materials that research in this area is actively developing and is promising. Many researchers report high accuracy of human identification by EEG. However, in such cases, a small volume of test samples or a small number of subjects are usually used, or the method was tested under ideal conditions (as in the case of \"error-free\" recognition based on the CERERE method [15]). In addition, the training or identification procedure takes a lot of time. Nevertheless, the uniqueness of EEG indicators is very high. The method proposed in this paper is more practice-oriented and belongs to the second of the listed categories based on movement incentives.