AI 中文总结
本研究提出一种零样本跨被试情感回归方法，将EEG-fNIRS情感轨迹分解为共享与个体结构，利用alpha同步性标记校准，显著优于基线，并验证了该标记的稳健性。
AI 中文摘要
从生理信号中进行逐秒的连续效价-唤醒度估计通常在依赖被试的设定下研究，即模型在训练时见过与后续评估相同的个体的带标签数据。我们研究更困难的零样本跨被试变体，在一个同步的EEG-fNIRS数据集上：预测模型从未见过其标签的被试的原始尺度（[1, 255]）效价和唤醒度轨迹，仅给定这些被试在观看与训练被试（不重叠）相同的视频刺激时的无标签EEG/fNIRS记录。我们将情感轨迹分解为两部分：观看相同刺激的被试间共享的结构，以及针对每个测试被试从无标签EEG标记（alpha频段跨通道同步性）估计的个体结构，该标记在尺度中点附近重新缩放共享轨迹。我们在四个独立维度上验证逐被试校准机制：标记与每个被试真实最优增益之间的留一被试相关性、与非线性替代方案的功能形式比较、重复留四被试的组件消融以隔离流程中每个部分的贡献，以及界定逐被试缩放剩余提升空间的上限分析。在留出被试上，模型在两个评估批次中达到总体MAE 25.96 / 22.80（效价21.94 / 19.6，唤醒度29.98 / 26.0），远低于同一被试独立划分下报告的EEGNet和ASAC-Net基线（原始尺度得分分别为60.6和55.0）。我们进一步报告了跨模型架构、特征表示和预测目标的系统性负面结果搜索，发现没有任何信号能优于单一的alpha同步性标记。
英文摘要
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.
