Classification models for P300 evoked potentials

The paper is devoted to the problem of user's attention detection. It investigates the choice of a visual stimulus by the electroencephalogram (EEG) with the evoked potentials related to the event, P300, highlighted in it. The electrical brain potentials are measured while the user is observing visual stimuli. The goal is to select a stimulus which causes the maximum brain response. A classification model detects if there is a P300 potential in an EEG segment. Various classification models for event-related potentials are compared. The paper proposes a method of data augmentation to improve the quality of classification. Computational experiments use an original real-world dataset of P300 potentials. This dataset was collected on 60 healthy users who are presented with visual stimuli. It is released to the public access.