Assessment of cognitive load in the context of neurosurgery.

Purpose: Image-guided neurosurgery demands precise depth perception to minimize cognitive burden during intricate navigational tasks. Existing evaluation methods rely heavily on subjective user feedback, which can be biased and inconsistent. This study uses a physiological measure via electroencephalography (EEG), to quantify cognitive load when using novel dynamic depth-cue visualizations. By comparing dynamic versus static rendering techniques, we aim to establish an objective framework for assessing and validating visualization strategies beyond traditional performance metrics.

Methods: Twenty participants (experts in brain imaging) navigated to specified targets within a computed tomography angiography (CTA) volume using a tracked 3D pointer. We implemented three visualization methods (shading, ChromaDepth, aerial perspective) in both static and dynamic modes, randomized across 80 trials per subject. Continuous EEG was recorded via a Muse headband; raw signals were preprocessed and theta-band (4-7 Hz) power extracted for each trial. A two-way repeated measures ANOVA assessed the effects of visualization type and dynamic interaction on theta power.

Results: Dynamic visualization conditions yielded lower mean theta-band power compared to static conditions (Δ = 0.057 V2/Hz; F (1,19) = 6.00, p = 0.024), indicating reduced neural markers of cognitive load. No significant main effect was observed across visualization methods, nor their interaction with dynamic mode. These findings suggest that real-time feedback from pointer-driven interactions may alleviate mental effort regardless of the specific depth cue employed.

Conclusion: Our exploratory results demonstrate the feasibility of using consumer-grade EEG to provide objective insights into cognitive load for surgical visualization techniques. Although limited by non-surgeon participants, the observed theta-power reductions under dynamic conditions support further investigation. Future work should correlate EEG-derived load measures with performance outcomes, involve practising neurosurgeons, and leverage high-density EEG or AI-driven adaptive visualization to refine and validate these preliminary findings.