arXiv:2610.28422v1 Announce Type: new Abstract: Cross-subject EEG emotion recognition now reports accuracies above 90% on stimulus-defined benchmarks such as SEED, and the field has examined several ways in which such numbers can be inflated, attributing inflation to subject identity, to data partitioning, and to repeated stimulus presentation. We observe that one variable has been left out. On these datasets the emotion label is assigned to the video at design time, so any encoder that recovers which video was shown obtains the label for free. We adapt three representation-level shortcut diagnostics to a cohort of published contrastive cross-subject encoders, and find that stimulus identity is linearly recoverable at six to fifteen times chance. We then measure its impact on the reported accuracy by holding out subjects and stimuli jointly, and find that each released classifier loses two to forty-four points once the test videos are unseen, so a headline obtained under subject hold-out alone is not a measure of cross-subject emotion decoding.