Cisotto Giulia, Zancanaro Alberto, Zoppis Italo F, Manzoni Sara L
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Department of Information Engineering, University of Padova, Padova, Italy.
Front Neuroinform. 2024 Dec 20;18:1459970. doi: 10.3389/fninf.2024.1459970. eCollection 2024.
Modeling multi-channel electroencephalographic (EEG) time-series is a challenging tasks, even for the most recent deep learning approaches. Particularly, in this work, we targeted our efforts to the high-fidelity reconstruction of this type of data, as this is of key relevance for several applications such as classification, anomaly detection, automatic labeling, and brain-computer interfaces.
We analyzed the most recent works finding that high-fidelity reconstruction is seriously challenged by the complex dynamics of the EEG signals and the large inter-subject variability. So far, previous works provided good results in either high-fidelity reconstruction of single-channel signals, or poor-quality reconstruction of multi-channel datasets. Therefore, in this paper, we present a novel deep learning model, called hvEEGNet, designed as a hierarchical variational autoencoder and trained with a new loss function. We tested it on the benchmark Dataset 2a (including 22-channel EEG data from 9 subjects).
We show that it is able to reconstruct all EEG channels with high-fidelity, fastly (in a few tens of epochs), and with high consistency across different subjects. We also investigated the relationship between reconstruction fidelity and the training duration and, using hvEEGNet as an anomaly detector, we spotted some data in the benchmark dataset that are corrupted and never highlighted before.
Thus, hvEEGNet could be very useful in several applications where automatic labeling of large EEG dataset is needed and time-consuming. At the same time, this work opens new fundamental research questions about (1) the effectiveness of deep learning models training (for EEG data) and (2) the need for a systematic characterization of the input EEG data to ensure robust modeling.
对多通道脑电图（EEG）时间序列进行建模是一项具有挑战性的任务，即使对于最新的深度学习方法也是如此。特别是在本研究中，我们致力于对这类数据进行高保真重建，因为这对于诸如分类、异常检测、自动标注和脑机接口等多种应用至关重要。
我们分析了近期的研究发现，EEG信号的复杂动态特性和个体间的巨大差异严重挑战了高保真重建。到目前为止，先前的研究要么在单通道信号的高保真重建方面取得了良好效果，要么在多通道数据集的低质量重建方面有所成果。因此，在本文中，我们提出了一种名为hvEEGNet的新型深度学习模型，它被设计为分层变分自编码器，并使用新的损失函数进行训练。我们在基准数据集2a（包括来自9名受试者的22通道EEG数据）上对其进行了测试。
我们表明，它能够以高保真度快速（在几十轮训练中）重建所有EEG通道，并且在不同受试者之间具有高度一致性。我们还研究了重建保真度与训练持续时间之间的关系，并且使用hvEEGNet作为异常检测器，我们在基准数据集中发现了一些之前从未被发现的损坏数据。
因此，hvEEGNet在需要对大型EEG数据集进行自动标注且耗时的多种应用中可能非常有用。同时，这项工作开启了关于（1）深度学习模型训练（针对EEG数据）的有效性以及（2）对输入EEG数据进行系统表征以确保稳健建模的必要性的新的基础研究问题。
