Learning Event Ownership via Finite-State Action-Path Marginalization for Irreversible Command Decoding from Two-Channel EOG

Continuous facial-gesture control from few-channel electrooculography (EOG) requires distinguishing both action classes and event identities: incoming evidence may continue an existing event or begin a new one. Frame-wise classifiers with fixed post-trigger cooldowns do not explicitly model this distinction and can suppress closely spaced commands. We introduce Action-Constrained Event Decoding (ACED), which separates action classification from event ownership. A learned ownership policy selects among accumulating evidence, emitting a command, and re-arming, replacing fixed cooldown timers. For ambiguous action prefixes, supervision targets an intermediate class and defers the final label until the command grammar permits disambiguation. Training exactly marginalizes over all supervision-compatible paths satisfying an event grammar that requires one emission per supervised event and excludes premature triggers. At inference, the decoder operates causally, never revises past outputs, and guarantees at most one emission per inferred event identity. Experiments show that ACED recovers closely spaced commands suppressed by cooldown baselines and reduces false-trigger rates. The same decoding logic transfers to public datasets with different action taxonomies through task-specific retraining. We further quantify the effects of cross-wear signal drift and address it with robust training recipes.