ArticleBrain topography2026
Topographic Reorganization of EEG Complexity During Visual Mental Imagery: Insights from Lempel-Ziv Complexity in High-Density EEG.
Article in Brain topography, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Visual mental imagery is the process of reconstructing perceptual experience without sensory input. How the brain performs this process is poorly understood, particularly from the perspective of conventional linear EEG analysis. This study aims to evaluate if the two non-linear EEG complexity measures-Lempel-Ziv Complexity (LZC) and Higuchi Fractal Dimension (HFD)-can differentiate between perception and imagination and if they can be used as objective indices of neural separability of mental imagery. LZC and HFD were extracted from 62 scalp EEG channels in 46 healthy adults performing the PerceiveImagine paradigm (Li and Fan 2024), after wideband Picard ICA decomposition (1-200 Hz), which was used to make residual artefacts explicit rather than to remove components, with edge-channel EMG monitoring for artefact control. Statistical analyses included cluster-based permutation testing (Maris and Oostenveld 2007), Hotelling T², and leave-one-subject-out cross-validation (LOSO-CV). Broadband LZC topography differed between perception and imagination (cluster p = 0.005; Hotelling F = 3.08, p = 0.002, V = 0.28). LOSO-CV classification reached AUC = 0.811 (95% CI: [0.775, 0.847]). The classifier's AUC exceeded a label-permuted baseline by a wide margin (t(45) = 16.30, p < 0.001). The a priori low-gamma LZC hypothesis was not supported, with no significant difference at the occipital ROI (p = 1.0, d = - 0.13). At the scalp level, EEG complexity features are associated with a topographic redistribution rather than a global magnitude change: imagery shows a relative posterior-to-frontal shift in broadband LZC. Because these patterns are scalp-recorded, they characterise the spatial distribution of complexity rather than establishing the underlying cortical sources or the direction of information flow. Objective decoding-confidence labels provide a more usable training signal than the (invariant) self-report available in this dataset, indicating future potential for imagery-quality indexing rather than immediate translational readiness.
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