AI 中文总结
针对冻结EEG表征中的采集伪影，提出测量门控来源衰减（MGPA），利用测量对比定义门控区域，在保留信息约束下最小化移动，无需重训练即可降低来源可预测性并保持或提升任务性能。
AI 中文摘要
冻结的脑电（EEG）表征既保留了采集特征，也保留了神经活动。仅凭来源可预测性并不能确定应去除什么：它可能反映测量效应，也可能反映不应被抹除的真实生物学和群体差异。我们提出测量门控来源衰减（MGPA），其原理基于：测量证据决定校正可能作用的区域，而保留信息决定其应追求的目标。成对的测量对比定义了一个门，在门外不发生任何改变；在门内，来源分数被移动到保留坐标已预测的值：对于固定的仿射分数，这保持了与这些坐标设定的任何目标相同的信息，并且需要最小的期望平方移动。闭式解和批评者引导的迭代构造在无需来源身份或编码器重新训练的情况下应用该方法。三项研究在逐渐偏离其假设的条件下测试了这一原理。在受控参考变化下（其中来源-任务关联已知），MGPA将来源可预测性降至接近随机水平，而任务性能保持不变；而擦除预测来源的信息（LEACE）将冻结任务AUROC从0.753降至0.656，同时几乎不影响来源；消融实验将衰减归因于门的方向以及条件目标所需的2.7倍更少的移动。在来自不同设备和电极的记录中，迭代校正降低了来源可访问性，同时保持或提高了任务性能。最后，一个在某一任务上选择并直接复用于其他两个任务现有头的迭代映射，在设备标签偏移的最坏关联AUROC上比LEACE提高了0.057和0.019，但在训练关联保持期间对这些头造成了一定代价。可复用的校正的价值体现在：当采集线索不再可靠时，现有预测器的行为变化，而不仅仅是探针能读取的内容。
英文摘要
Frozen EEG representations retain acquisition signatures as well as neural activity. Source predictability alone does not identify what should be removed: it can reflect measurement effects or genuine biological and population differences, which should not be erased. We propose Measurement-Gated Provenance Attenuation (MGPA), built on one principle: measurement evidence determines where correction may act, and preserved information determines what it should aim for. Paired measurement contrasts define a gate outside which nothing changes; inside it, the source score is moved to the value the preserved coordinates already predict: for a fixed affine score, this keeps the same information as any target set by those coordinates and needs the least expected squared movement. Closed-form and critic-guided iterative constructions apply it without source identity or encoder retraining. Three studies test the principle at increasing distance from its assumptions. Under controlled reference changes, where the source-task association is known, MGPA brings source to near chance with task performance unchanged, whereas erasing what predicts source (LEACE) lowers frozen-task AUROC from .753 to .656 while barely touching source; ablations attribute the attenuation to the gate's directions and 2.7x less movement to the conditional target. Across recordings from different devices and electrodes, iterative correction lowers source accessibility while preserving or improving task performance. Finally, one iterative map selected on one task and reused unchanged on existing heads for two others raises their worst-association AUROC (lowest over device-label shifts) by .057 and .019 over LEACE, at a cost to those heads while the training association holds. A reusable correction shows its value in how an existing predictor behaves once acquisition cues stop being reliable, not only in what a probe can read.
Comments23 pages, 4 figures, 11 tables. Code: https://github.com/sneddy/mgpa-paper
