An Evidence-Aware Framework for EEG Microstate Analysis: Improved Sensitivity to Alzheimer's Disease and Ageing

Abstract

Electroencephalography (EEG) microstate analysis commonly converts each scalp topography into a winner-take-all hard label and summarises the resulting sequence using duration, occurrence, coverage, transitions, and symbolic complexity. Although interpretable, this readout discards evidence strength, assignment ambiguity, and low-confidence periods. We introduce a template evidence trajectory framework that retains, at each sampled Global Field Power (GFP) peak, the evidence for all templates or subject-specific topographic communities. Conventional hard labels are treated as a compressed readout of this multivariate trajectory. We derive two evidence-aware extensions of classical descriptors: high-evidence episode duration and episode rate, which quantify temporal clustering and fragmentation of strong state evidence, and null-state Lempel-Ziv complexity (LZC), which explicitly encodes insufficient-evidence periods. We evaluated the framework across four resting-state EEG datasets spanning Alzheimer's disease and age-related variation. Fixed-state models included K-means, AAHC, and HMMs at K = 4 and K = 7, together with adaptive subject-specific Leiden and Infomap communities. Across 32 dataset-model cases, trajectory-derived duration effects exceeded matched hard-label effects in all cases at the representative setting and in 30-32 cases across a broader parameter grid. Null-state LZC improved over traditional LZC in most cases, while classification showed modest but consistent gains for trajectory or combined features. Retaining template evidence therefore provides a more sensitive readout of EEG topographic state dynamics while remaining compatible with conventional microstate analysis.