الملخص
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.
الكلمات المفتاحية
الموضوع
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- المجلة
- غير متاح
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اقتبس هذه المقالة
APA 7
Wang, X., Wang, B., Chang, S., Yuan, H., Qi, X., & Zhang, X. (2026). Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS. https://omanscience.com/ar/articles/modeling-shared-and-individual-structure-for-cross-subject-continuous-affect-regression-from-eeg-fnirs
MLA 9
Wang, Xuan, et al. "Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS." https://omanscience.com/ar/articles/modeling-shared-and-individual-structure-for-cross-subject-continuous-affect-regression-from-eeg-fnirs.
شيكاغو (المؤلف–التاريخ)
Wang, Xuan, Bing Wang, Shuai Chang, Hao Yuan, Xinbo Qi, and Xinyue Zhang. 2026. "Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS." https://omanscience.com/ar/articles/modeling-shared-and-individual-structure-for-cross-subject-continuous-affect-regression-from-eeg-fnirs.
هارفارد
Wang, X., Wang, B., Chang, S., Yuan, H., Qi, X. and Zhang, X. (2026) 'Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS', Available at: https://omanscience.com/ar/articles/modeling-shared-and-individual-structure-for-cross-subject-continuous-affect-regression-from-eeg-fnirs.
فانكوفر
Wang X, Wang B, Chang S, Yuan H, Qi X, Zhang X. Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS. https://omanscience.com/ar/articles/modeling-shared-and-individual-structure-for-cross-subject-continuous-affect-regression-from-eeg-fnirs
IEEE
X. Wang, B. Wang, S. Chang, H. Yuan, X. Qi, and X. Zhang, "Modeling Shared and Individual Structure for Cross-Subject Continuous Affect Regression from EEG-fNIRS," https://omanscience.com/ar/articles/modeling-shared-and-individual-structure-for-cross-subject-continuous-affect-regression-from-eeg-fnirs.