FROM PHYSIOLOGICAL COMPOSITION TO AFFECTIVE GEOMETRY IN EEG REPRESENTATIONS

Emotion is associated with joint activity across multiple brain regions. However, the regional and spectral composition of emotion-related activity remains difficult to characterize explicitly in high-dimensional EEG representations. In this work, we propose a physiological compositional framework that expresses emotion-related EEG activity through a shared vocabulary of region–frequency motifs. For each EEG instance, we estimate motif activations from EEG foundation-model representations, guided by each motif’s predefined scalp region and spectral activity within its frequency band. Competition among motifs reduces redundant evidence assignment, and their activations together form an instance-specific physiological composition. The resulting representation provides a shared physiological space for comparing affective geometries across foundation models. Across all nine dataset–model combinations, physiologically structured motifs outperform randomized mappings, supporting the presence and predictive relevance of physiological organization in the learned composition. And two independently pretrained foundation models also recover highly similar affective geometry, with similarities of 0.976 in emotion distances and 0.935 in centroid angles. Together, these results establish physiological composition as a common, interpretable space for comparing EEG foundation models not only by decoding performance, but also by how they organize affective structure.