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arXiv AI By Xuan Wang, Bing Wang, Shuai Chang, Hao Yuan, Xinbo Qi, Xinyue Zhang

Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS

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The paper presents a method for zero‑shot cross‑subject continuous affect regression using synchronized EEG‑fNIRS data. It decomposes affect trajectories into a shared component across subjects and an individual component derived from a label‑free alpha‑band cross‑channel synchrony marker, which rescales the shared trajectory for each test subject. Extensive validation—including leave‑one‑subject‑out correlation, functional‑form comparison, component ablation, and ceiling analysis—shows the approach achieves lower mean absolute errors than EEGNet and ASAC‑Net baselines on unseen subjects.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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