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

Chiossi, Francesco; Ou, Changkun; Gerhardt, Carolina; Putze, Felix; Mayer, Sven · 2024 · OpenAlex-citations

DOI: 10.1016/j.ijhcs.2024.103433

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Summary

This paper addresses the challenge of cognitive overload in Virtual Reality (VR) environments, where excessive visual stimuli can disrupt the balance between internal attention (working memory, mental processing) and external attention (processing environmental cues). Current VR systems often fail to account for users' working memory loads, leading to either over-stimulation or under-stimulation. The authors propose an adaptive VR system that uses Electroencephalography (EEG) to detect shifts in attentional states and dynamically adjusts the visual complexity of distracting elements to maintain optimal cognitive performance. The study employed a visual working memory N-Back task within a VR environment, which requires both internal maintenance of information and external vigilance for new stimuli. Participants wore a 64-channel EEG headset while interacting with the system. The adaptive mechanism monitored frontal theta and parietal alpha frequency bands, which are established correlates of working memory and attentional gating. Based on these signals, the system adjusted the "Stream" of non-player characters (NPCs) passing by the user, serving as controlled visual distractors. Two adaptation modes were tested: a "Positive Adaptation" designed to support internal attention by reducing distractions when internal load was high, and a "Negative Adaptation" (reverse adaptation) that increased distractions. Additionally, the researchers trained a Linear Discriminant Analysis (LDA) model to classify internal versus external attention states in real-time. The results demonstrated that the EEG-based adaptive system effectively improved task performance and reduced perceived workload compared to the reverse adaptation condition. Specifically, balancing for internal attention allowed users to maintain focus without being overwhelmed by external stimuli. The LDA model achieved a classification accuracy of 79.4% in distinguishing between internal and external attention states using EEG frequency features, validating the feasibility of real-time detection. The study confirmed that frontal theta and parietal alpha bands are reliable indicators for adjusting dynamic visual complexity in VR settings. The significance of this work lies in its demonstration that physiological computing can be used to create implicit, adaptive VR experiences that balance distraction management and user engagement. By leveraging EEG correlates to adjust peripheral environmental factors rather than core task features, the system supports cognitive efficiency without altering the primary task structure. This approach offers a novel method for enhancing productivity and immersion in VR applications, providing a foundation for future research into real-time, neuroergonomic adaptive systems that prevent cognitive overload while maintaining user focus.

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• distraction detection algorithms