This article proposes a scheme for multifactor authentication based on electroencephalography (EEG) signal analysis. A solution for EEG signal acquisition and recording of acquisition results has been implemented, a machine learning model has been developed and trained, then a classifier that determines the user's login procedure familiarity has been built, and a solution to carry out the described experiments has been implemented, in the form of a mobile application. Besides, a multifactor authentication system, based on the EEG signal combined with user image verification, using the brain–computer interface system with a single EEG electrode based on the NeuroSky MindWave device was proposed. Based on the defined scenarios, experiments were conducted, followed by a survey on the research group, and the obtained results were analyzed. In the case of an experiment related to login simulation, a high classification accuracy rate was obtained, both for the classifier itself (83.33%) and the proposed user authentication system (77.78%). Analyzing the results of the EEG signal recording used in the classification, it seems that the proposed solution is promising not only due to the high accuracy and a low false rejections rate but also through confirmed associations in the analysis of brain wave signal, corresponding to the results of research in the literature. A proposed multi-factor authentication system based on image selection and EEG analysis can be implemented in many areas as a modern solution in securing IT systems.

Increasing societal awareness of mental health challenges has significantly reduced stigma surrounding psychological disorders, encouraging greater numbers of individuals to seek professional support, which has placed unprecedented pressure on mental health services, with institutions ranging from educational establishments to emergency services implementing systematic screening protocols to identify individuals requiring intervention. However, the growing demand for rapid, accurate diagnosis continues to strain limited professional resources. Our study introduces an innovative machine learning framework for mental disorder detection using electroencephalography (EEG) signals processed through Welch’s power spectral density estimation. Unlike conventional Fast Fourier Transform (FFT) approaches, our method generates refined two-dimensional spectrograms capturing brain wave amplitudes (in dB) alongside precise peak frequency identification. This computationally efficient periodogram variant enables robust feature extraction suitable for real-time diagnostic applications while reducing model training overhead. Preliminary analysis demonstrates the Welch Transform’s superior signal characterization compared to standard FFT periodograms, revealing distinct neurophysiological patterns associated with various mental health conditions. The approach maintains high computational efficiency, supporting potential deployment in clinical screening environments.