Do viewer-specific physiological signals improve continuous emotion regression beyond the stimulus-driven response shared across viewers? We examine this question by predicting emotion ratings for new viewers watching videos that also appear in the training set. As a stimulus-only baseline, we define a video-time prior:the smoothed median of training labels for each video at each second. We then combine this prior, using a fixed fusion weight, with a graph-based model of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals. On MER-PS 2026, five-fold cross-validation grouped by participant over 24 participants yields a mean absolute error (MAE) of 29.01 for fusion, compared with 29.06 for the prior alone. On an external cohort of four participants excluded from training, the corresponding MAEs are 27.72 and 28.04. In this cohort, most of the MAE reduction relative to a global-constant baseline comes from the prior, that is, from video identity and playback time alone.Fusion lowers MAE for three of the four held-out participants and for nine of the fifteen videos. The added value of physiological signals is therefore small and varies across participants and videos. Because the model uses physiological signals recorded after each prediction time, it supports only offline prediction. Generalization to unseen videos remains untested.

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