The ability of Deep Learning to process and extract relevant information in complex brain dynamics from raw EEG data has been demonstrated in various recent works. Deep learning models, however, have also been shown to perform best on large corpora of data. When processing EEG, a natural approach is to combine EEG datasets from different experiments to train large deep-learning models. However, most EEG experiments use custom channel montages, requiring the data to be transformed into a common space. Previous methods have used the raw EEG signal to extract features of interest and focused on using a common feature space across EEG datasets. While this is a sensible approach, it underexploits the potential richness of EEG raw data. Here, we explore using spatial attention applied to EEG electrode coordinates to perform channel harmonization of raw EEG data, allowing us to train deep learning on EEG data using different montages. We test this model on a gender classification task. We first show that spatial attention increases model performance. Then, we show that a deep learning model trained on data using different channel montages performs significantly better than deep learning models trained on fixed 23- and 128-channel data montages.

翻译：深度学习从原始脑电图数据中处理并提取复杂脑动力学相关特征的能力已在近年多项工作中得到验证。然而，深度学习模型在大规模数据语料库上表现最佳。在处理脑电图时，一种自然策略是将不同实验的脑电图数据集合并，以训练大型深度学习模型。但多数脑电图实验采用定制化通道导联配置，需将数据转换至统一空间。先前方法通过原始脑电图信号提取目标特征，并致力于在跨脑电图数据集间建立公共特征空间。此法虽合理，却未能充分挖掘原始脑电图数据的潜在丰富性。本文探索将空间注意力机制应用于脑电图电极坐标，实现原始脑电图数据的通道归一化，从而支持在不同导联配置的脑电图数据上训练深度学习模型。我们在性别分类任务上测试该模型：首先证明空间注意力可提升模型性能；继而表明，相较于固定23通道与128通道数据导联配置训练的深度学习模型，采用不同通道导联配置数据训练的模型性能显著更优。