In the presented work, a wavelet analysis of the patient’s electroencephalogram was performed, followed by the construction of a scalogram. This approach made it possible to identify the frequency components of the electroencephalogram and conduct a comprehensive analysis of them. The obtained results can be used to monitor the state of the patient’s brain activity. The main purpose of the study is to analyze and filter the signal to determine the main composite frequencies of the electroencephalographic signal, on the basis of which it is possible to determine the state of brain activity at certain points in time, which may reflect various cognitive processes, emotional states and concentration levels of the patient. Methodology. An electroencephalogram of the patient is taken, and using wavelet transformations, a set of frequencies at each moment of time with their amplitude is obtained. Then the signal is filtered from noise, and the wavelet transform is re-applied to obtain a set of frequencies. After that, the frequencies are analyzed at each point in time, and based on the data, the state of the patient’s brain activity is determined. The results of the study. As a result of the study of the electroencephalographic signal analysis process, it was possible to filter the original signal from noise and identify the main frequencies that make up the electroencephalographic signal. After that, on the basis of frequencies, different states of consciousness of the patient at each moment of time are determined. The scope of application. The introduction of the principles of wavelet analysis into the architecture of programmable logic integrated circuits (FPGAs) for analyzing captured signals using ultrasonic sensors on FPGAs, thus implementing an autonomous device.