Аннотация:This study explores the feasibility of developing a dynamic cognovisor capable of recognizing cognitive states and transitions using fMRI data. Data were collected from 31 participants performing spatial and verbal tasks during fMRI scanning and were preprocessed using a nine-step algorithm for artifact removal and denoising. Three types of classification problems were examined, with machine learning methods and dimensionality reduction techniques applied to classify activity states. The best-performing models were identified for each classification problem, providing insights into their applicability. Notably, binary classification of resting versus active states achieved good quality with relatively simple methods. A key finding underscores the importance of accounting for temporal history of the signal prior to the prediction moment to improve model performance.
