Introduction. Recently, machine learning methods, which are core components of artificial intelligence, have gained popularity in analyzing neurophysiological data. Functional near-infrared spectroscopy (fNIRS) is actively used to study neurocognitive mechanisms. This technology for recording hemodynamic data has a number of advantages, including spatial resolution, non-invasiveness, and the feasibility to conduct studies in natural settings, which has made the technology popular among researchers. Theoretical justification. The analysis of fNIRS results relies on the sequence and selected methods for preliminary processing of raw data, as well as on the classification models employed. This review evaluates various preprocessing methods and examines the approaches to classifying fNIRS data. An essential aspect of preprocessing involves detecting and eliminating physiological artifacts from raw data, utilizing algorithms such as filtering, signal whitening, principal component analysis (PCA) and independent component analysis (ICA), short-channels removal. Methods such as wavelet filtering, spline interpolation, and Kalman filtering are employed to address motion artifacts. Discussion. The review aims to provide an in-depth exploration of machine learning methods, specifically recurrent neural networks (RNN) and convolutional neural networks (CNN), which have been used in various studies for analyzing fNIRS data. The review highlights that leveraging deep learning neural networks can streamline signal preprocessing while achieving higher accuracy compared to traditional approaches in processing neurocognitive data.

Mathematical anxiety (MA) negatively affects all aspects of activities related to manipulating numbers, both in education and in everyday life. MA is negatively associated with mathematical self-efficacy, subjective value of mathematics, attitude, and interest towards mathematics. Individuals with pronounced MA have a lower need for cognitive load and reflective thinking. Among the predictors of MA, cognitive, personality, and social aspects can be identified. The question of the brain mechanisms of MA remains relevant. The review provides an analysis of studies on the neurophysiological correlates of mathematical anxiety using modern psychophysiological methods: electroencephalography, magnetic resonance imaging (MRI). The results of studies using these methods are inconsistent. When studying MA, attention is paid to brain structures associated with the processing of both emotional information and cognitive processes. The analysis of the literature has shown that when implementing corrective measures, it is important to consider the lack of a unified theoretical approach, which raises questions about the causes of MA.