Spatial Encoding Geometry Determines the Maps and Transitions Recovered by Deep EEG Microstate Models

Unsupervised deep clustering is widely used for discovery in neuroscience, yet model choice is treated as an implementation detail. Models sharing the same data and evaluation metrics are assumed to recover equivalent structure. This assumption is tested in EEG microstate analysis, where discovered states feed biomarker studies. Two variational-deep-embedding models are compared on 203 paired eyes-closed recordings (LEMON). The comparison is centred on how the topography is spatially encoded: one model interpolates scalp topographies onto a 2-D image grid, the other learns on the anatomical electrode graph, with shared preprocessing, peak extraction, and evaluation throughout. As deployed, the two pipelines report a two-fold difference in temporal parameters: mean dwell 70.2 against 143.2 ms and occurrence 3.57 against 1.65 s⁻¹. Decoding each model's own recovered topographies with a common backfit removes that difference: mean dwell 70.2 against 71.0 ms and occurrence 3.57 against 3.37 s⁻¹. The reported divergence is therefore a property of the decode rather than of the encoding. What the encoding does change is the recovered maps. Under the common decode the graph model attains the higher backfit global explained variance, 0.618 against 0.541. Internal cluster metrics do not track this: the image model attains the higher silhouette (0.175 against 0.170, significant after Holm correction), and the indices are computed in different input geometries. These findings indicate that spatial encoding changes the recovered maps, the latent geometry, and the transition statistics of the sequences, but not the dwell and occurrence of the states, and that reported microstate parameters should state the backfit and the decode that produced them.