Assessment of changes in neural activity during acquisition of spatial knowledge using EEG signal classification

Abstract

Objective. This study explored the classification of electroencephalography (EEG) signals to assess changes in neural activity as individuals performed a training task in a virtual environment simulator. Commonly, task behavior and perception are used to assess a trainee’s ability to perform a task, however, changes in cognition are not usually measured and could be important to provide a true indication of an individual’s level of knowledge or skill. Approach. In this study, 15 participants acquired spatial knowledge via 60 navigation trials (divided into ten blocks) in a novel virtual environment. Time performance, perceived certainty, and EEG signal data were collected. Main results. A significant increase in alpha power and classification accuracy of EEG data from block 1 against blocks 2–10 was observed and stabilized after block 7, while time performance and perceived certainty measures improved and stabilized after block 5 and 6, respectively. Significance. Results suggest that changes in neural activity, which may reflect an increase in cognitive efficiency, could provide additional insight beyond time performance and perceived certainty.