Designing and Evaluating an Adaptive Virtual Reality System using EEG Frequencies to Balance Internal and External Attention States

Virtual reality (VR) finds various applications in productivity, entertainment, and training, often requiring substantial working memory and attentional resources. Effective task performance in VR relies on prioritizing relevant information and suppressing distractions through internal attention. However, current VR systems fail to account for the impact of working memory loads, leading to over or under-stimulation. In this work, we designed an adaptive system using Electroencephalography (EEG) correlates of external and internal attention to support working memory tasks. Participants engaged in a visual working memory N-Back task, where we adapted the visual complexity of distracting elements. Our study demonstrated that EEG frontal theta and parietal alpha frequency bands effectively adjust dynamic visual complexity. The adaptive system improved task performance and reduced perceived workload compared to a reverse adaptation. Furthermore, we trained a Linear Discriminant Analysis (LDA) model and achieved a classification accuracy of 79.4% for distinguishing internal and external attention states using EEG frequency features, demonstrating the feasibility of EEG-based models for real-time attention state detection. These results highlight the potential of EEG-based adaptive systems to balance distraction management and maintain user engagement without causing cognitive overload. • Developed a VR system using EEG to balance performance and engagement. • Improved task performance with subtle, natural adaptations to environment factors. • Achieved 79.4% accuracy classifying attention states using an LDA model.