基于EEG-fNIRS的跨被试连续情感回归中共享与个体结构建模

# Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS
Source: [https://arxiv.org/abs/2610.02796](https://arxiv.org/abs/2610.02796)
[View PDF](https://arxiv.org/pdf/2610.02796)

> Abstract:Continuous, second\-by\-second valence\-arousal estimation from physiological signals is typically studied in a subject\-dependent setting, where the model sees labeled data from the same person it is later evaluated on\. We study the harder zero\-shot cross\-subject variant on a synchronized EEG\-fNIRS dataset: predict raw\-scale \(\[1, 255\]\) valence and arousal trajectories for subjects whose labels the model never observes, given only their unlabeled EEG/fNIRS recordings while watching the same video stimuli as a disjoint set of training subjects\. We decompose the affect trajectory into a structure shared across subjects who watch the same stimuli and an individual structure estimated for each test subject from a label\-free EEG marker \(alpha\-band cross\-channel synchrony\), which rescales the shared trajectory around the scale midpoint\. We validate the per\-subject calibration mechanism on four independent axes: leave\-one\-subject\-out correlation between the marker and each subject's true optimal gain, a functional\-form comparison against non\-linear alternatives, a repeated leave\-4\-out component ablation isolating each part of the pipeline's contribution, and a ceiling analysis bounding the remaining headroom for per\-subject scaling\. On held\-out subjects, the model reaches an overall MAE of 25\.96 / 22\.80 across two evaluation batches \(valence 21\.94 / 19\.6, arousal 29\.98 / 26\.0\), well below EEGNet and ASAC\-Net baselines reported for the same subject\-independent split \(raw scale score 60\.6 and 55\.0 respectively\)\. We further report a systematic negative\-result search across model architectures, feature representations, and prediction targets that found no signal able to improve on the single alpha\-synchrony marker\.

## Submission history

From: Xuan Wang \[[view email](https://arxiv.org/show-email/150f3d03/2610.02796)\] **\[v1\]**Fri, 2 Oct 2026 04:37:59 UTC \(333 KB\)