## 1 Introduction  
Traditional authentication mechanisms (passwords, qualified and unqualified electronic signatures, and confirmation via phone) are alienable from the owner; therefore, they are subject to the \"human factor\". These protection methods are unsafe for people with low computer literacy and those who neglect security issues. The problem of \"alienation\" of the authenticator is solved by linking keys and passwords to a person's biometric parameters. Many commercial solutions currently available are based on static biometric images (fingerprints, iris patterns, face, etc.). Their fundamental problem is that they are not secret. An intruder can falsify an open biometric image, so the biometric image used for authentication must be secret (combine a password and the user's individual characteristics). Secret biometric images can be formed when reproducing any subconscious movements by a person. For example, a person's keyboard handwriting reflects the characteristics of his hand movements when entering a password phrase or typing arbitrary text [1]. Keyboard handwriting images are considered uninformative and provide a relatively high percentage of incorrect solutions during identification and authentication. All dynamic biometric images, such as voice and handwritten passwords, are subject to this problem to varying degrees [2]. Almost any biometric image can be intercepted: a voice is recorded on a microphone, keyboard handwriting can be secretly tracked using keyloggers, fingerprints are left on objects, images of faces are on photographs, signatures are on paper, etc. Since technologies for generating adversarial examples are constantly being improved--generative neural networks based on the transformer architecture and adversarial learning methods [3] allow for a targeted enumeration of biometric images and minimize the complexity of an attack on a biometric system--theft of open biometrics is not a problem for a qualified attacker. There is a need to create an alternative authentication method that is free from the above-mentioned shortcomings. In this paper, it is proposed to use the features of the user's electroencephalogram (EEG) recorded during the process of entering a password (password phrase or text) on the keyboard to recognize the user's identity (Figure 1). EEG is a set of signals characterizing the electrical activity of the brain, recorded non-invasively using electrodes located on the surface of the head. Studies have shown that human EEG parameters are unique [4]. The set of EEG indicators of a subject can be considered as a vector of biometric parameters. Biometric EEG images are the most secure against any kind of attack. The falsification of these features is complicated because the EEG signals required for the synthesis and transformation of identifiers are difficult to intercept, and it is impossible to do this remotely or covertly (unlike keyboard handwriting or fingerprints left on objects). The advantages of the method also include the fact that it can be used by people with disabilities, and that the secret biometric image of the EEG can be changed at any time if it somehow has been compromised. Identification requires the human brain to reproduce a stable impulse. For this purpose, the organs of hearing, vision, etc., are stimulated. By changing the stimulus affecting the subject's brain at the time of identification, the nature of the EEG signals changes, so the stimulus plays the role of a password, and the features of the EEG are biometric features that depend on the password. The study of the brain is an area of active research; as a result of which, EEG recording equipment is constantly being modernized. There are various types of electrodes (dry, bridge, cup), each of which can operate in different conditions. The element base is being improved, due to which the cost of electroencephalographs is decreasing. In addition, artificial intelligence and big data analysis methods are actively developing.