Recent years have witnessed the successful development of health-related Internet-of-Things (IoT) devices, i.e., electroencephalographic (EEG), paving a ground-breaking way for better understanding the functionals in our brains. Despite massive research on EEG, there lacks an effective way to interpret complex EEG signals due to the shortage of informative EEG data, the challenge in capturing sophisticated connectivity patterns of EEG signals, and unfavorable results of the inherent noise associated with data collection. In this article, novel heterogeneous-domain deep learning is proposed to address these issues. Especially, we first propose a new scheme to extract the multilevel latent features using hybrid networks and provide two pathways for reconstructing the EEG signals. As a core part of the proposed method, the scheme considers the complex dependencies among adjacent EEG channels, spatiotemporal connectivity, and signal denoising. In addition, a novel consistency regularization method is proposed to enhance information sharing among the multilevel latent feature obtained from the labeled and unlabeled EEG samples, which is beneficial for both the information transfer and accelerating the training. Finally, we provide comprehensive case studies on the Lomonosov Moscow State University EEG data set, demonstrating that the proposed methods achieve superior performance than all the competing ones over a wide range of experimental settings.

中文翻译：

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Ahed：一种异构域深度学习模型，用于具有很少标记的脑电图数据的物联网智能健康


近年来，与健康相关的物联网（IoT）设备，即脑电图（EEG）的成功发展，为更好地了解我们大脑的功能铺平了突破性的道路。尽管对脑电图进行了大量研究，但由于缺乏信息丰富的脑电图数据、捕获脑电图信号复杂连接模式的挑战以及与数据收集相关的固有噪声的不利结果，缺乏解释复杂脑电图信号的有效方法。在本文中，提出了新颖的异构域深度学习来解决这些问题。特别是，我们首先提出了一种使用混合网络提取多级潜在特征的新方案，并提供了两种重建脑电图信号的途径。作为该方法的核心部分，该方案考虑了相邻脑电图通道、时空连接性和信号去噪之间的复杂依赖性。此外，提出了一种新的一致性正则化方法，以增强从标记和未标记脑电图样本获得的多级潜在特征之间的信息共享，这有利于信息传递和加速训练。最后，我们提供了关于罗蒙诺索夫莫斯科国立大学脑电图数据集的全面案例研究，证明所提出的方法在广泛的实验设置中比所有竞争方法取得了优越的性能。