坐标条件检测器-原子网络用于与蒙太奇无关的脑电通道补全：零样本迁移与机制图谱

中文摘要 AI 辅助

脑电图电极布局因设备而异，从消费级头戴设备到128通道网络，而学习型缺失电极补全方法假设固定布局和数据集内训练，因此无法应用于未见过的设备。我们提出了坐标条件检测器-原子网络（CC-DAN），一种自监督卷积字典模型，其原子是电极坐标的球谐函数，其检测器以坐标相关权重汇集观测电极，使其独立于电极数量、顺序和名称。在即插即用协议中，一个在五个公共数据集（132名受试者）上预训练的单一模型被零样本应用于九个保留集，通道数从8到128，并与最近邻和球面样条插值（SSI）、受试者内基准以及学习基线在信号误差、频带误差和下游任务上进行比较。当观测电极少于或等于5个、稀疏真实布局和半球缺失时，CC-DAN在几乎所有受试者中优于两种插值方法，并接近基准；在EGI网络和8至16通道设备上，它保持这一优势直到15个电极，而SSI在密集10-05蒙太奇上具有15个或更多电极时表现更优。CC-DAN对电极位置误差也最为鲁棒。均方误差增益并未转化为下游准确率，且MSE最优预测的幅度收缩会降低基于功率的度量，而方差匹配损失和增益校准可缓解这一问题。由此产生的机制图谱告诉实践者学习补全在何处有效，以及经典插值在何处仍是首选方法，即插即用协议允许一个预训练模型无需重新训练即可将未见设备连接到固定蒙太奇流程。

英文摘要

EEG electrode layouts differ across devices, from consumer headsets to 128-channel nets, and learned methods for completing missing electrodes assume a fixed layout and within-dataset training, so they cannot be applied to unseen devices. We propose the coordinate-conditioned detector-atom network (CC-DAN), a self-supervised convolutional dictionary model whose atoms are spherical-harmonic functions of electrode coordinates and whose detector pools observed electrodes with coordinate-dependent weights, making it independent of electrode number, order, and names. In a plug-and-play protocol, a single model pretrained on five public datasets (132 subjects) is applied zero-shot to nine held-out sets with 8 to 128 channels and compared with nearest-neighbor and spherical spline interpolation (SSI), a within-subject oracle, and learned baselines on signal error, band-wise error, and downstream tasks. With five or fewer observed electrodes, sparse real layouts, and hemispheric dropout, CC-DAN beats both interpolations in nearly all subjects and approaches the oracle; on the EGI net and 8- to 16-channel devices it keeps this advantage up to 15 electrodes, whereas SSI is superior with 15 or more electrodes on dense 10-05 montages. CC-DAN is also the most robust to electrode-position errors. Mean-squared-error gains do not carry over to downstream accuracy, and the amplitude shrinkage of MSE-optimal prediction degrades power-based measures, which a variance-matching loss and gain calibration mitigate. The resulting regime map tells practitioners where learned completion pays off and where classical interpolation remains the method of choice, and the plug-and-play protocol lets one pretrained model connect an unseen device to a fixed-montage pipeline without retraining.

发表机构

• Faculty of Engineering Science, Kansai University（关西大学理工学部）

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