Recently, deep learning-based electroencephalogram (EEG) analysis and decoding have attracted widespread attention for monitoring the clinical condition of users and identifying their intention/emotion. Nevertheless, the existing methods generally model EEG signals with limited viewpoints or restricted concerns about the characteristics of the EEG signals, and thus represent complex spectro-/spatiotemporal patterns and suffer from high variability. In this work, we propose the novel EEG-oriented self-supervised learning methods and a novel deep architecture to learn rich representation, including information about the diverse spectral characteristics of neural oscillations, the spatial properties of electrode sensor distribution, and the temporal patterns of both the global and local viewpoints. Along with the proposed self-supervision strategies and deep architectures, we devise a feature normalization strategy to resolve the intra-/inter-subject variability problem. We demonstrate the validity of our proposed deep learning framework on the four publicly available datasets by conducting comparisons with the state of the art baselines. It is also noteworthy that we exploit the same network architecture for the four different EEG paradigms and outperform the comparison methods, thereby meeting the challenge of the task-dependent network architecture engineering in EEG-based applications.

中文翻译：

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具有三重信息通路网络的面向脑电图的自我监督学习


近年来，基于深度学习的脑电图 （EEG） 分析和解码在监测用户的临床状况和识别其意图/情绪方面引起了广泛关注。然而，现有的方法通常对脑电信号进行建模，但视角有限或对脑电信号的特性关注有限，因此代表了复杂的光谱/时空模式，并具有很高的可变性。在这项工作中，我们提出了新颖的面向脑电图的自我监督学习方法和新颖的深度架构来学习丰富的表示，包括有关神经振荡的不同光谱特性、电极传感器分布的空间特性以及全局和局部视点的时间模式的信息。除了提出的自我监督策略和深度架构外，我们还设计了一种特征归一化策略来解决主体内/主体间的可变性问题。通过与最先进的基线进行比较，我们证明了我们提出的深度学习框架在四个公开可用的数据集上的有效性。同样值得注意的是，我们对四种不同的脑电图范式利用了相同的网络架构，并超越了比较方法，从而应对了基于脑电图的应用中任务依赖型网络架构工程的挑战。