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

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.