Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.

ABSTRACT: Within the broader context of improving interactions between artificial intelligence and humans, the question has arisen regarding whether auditory and rhythmic support could increase attention for visual stimuli that do not stand out clearly from an information stream. To this end, we designed an experiment inspired by pip-and-pop but more appropriate for eliciting attention and P3a-event-related potentials (ERPs). In this study, the aim was to distinguish between targets and distractors based on the subject's electroencephalography (EEG) data. We achieved this objective by employing different machine learning (ML) methods for both individual-subject (IS) and cross-subject (CS) models. Finally, we investigated which EEG channels and time points were used by the model to make its predictio

SUBMITTER: Mortier S 

PROVIDER: S-EPMC10708631 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature