Electroencephalogram (EEG) excels in portraying rapid neural dynamics at the level of milliseconds, but its spatial resolution has often been lagging behind the increasing demands in neuroscience research or subject to limitations imposed by emerging neuroengineering scenarios, especially those centering on consumer EEG devices. Current superresolution (SR) methods generally do not suffice in the reconstruction of high-resolution (HR) EEG as it remains a grand challenge to properly handle the connection relationship amongst EEG electrodes (channels) and the intensive individuality of subjects. This study proposes a deep EEG SR framework correlating brain structural and functional connectivities (Deep-EEGSR), which consists of a compact convolutional network and an auxiliary fully connected network for filter generation (FGN). Deep-EEGSR applies graph convolution adapting to the structural connectivity amongst EEG channels when coding SR EEG. Sample-specific dynamic convolution is designed with filter parameters adjusted by FGN conforming to functional connectivity of intensive subject individuality. Overall, Deep-EEGSR operates on low-resolution (LR) EEG and reconstructs the corresponding HR acquisitions through an end-to-end SR course. The experimental results on three EEG datasets (autism spectrum disorder, emotion, and motor imagery) indicate that: 1) Deep-EEGSR significantly outperforms the state-of-the-art counterparts with normalized mean squared error (NMSE) decreased by 1%–6% and the improvement of signal-to-noise ratio (SNR) up to 1.2 dB and 2) the SR EEG manifests superiority to the LR alternative in ASD discrimination and spatial localization of typical ASD EEG characteristics, and this superiority even increases with the scale of SR.

中文翻译：

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通过关联脑结构和功能连接实现深度脑电图超分辨率


脑电图（EEG）擅长描绘毫秒级的快速神经动态，但其空间分辨率往往落后于神经科学研究日益增长的需求，或者受到新兴神经工程场景（尤其是那些以消费脑电图设备为中心的场景）的限制。目前的超分辨率（SR）方法通常不足以重建高分辨率（HR）脑电图，正确处理脑电图电极（通道）和受试者强烈个性之间的连接关系仍然是一个巨大的挑战。本研究提出了一种关联大脑结构和功能连接的深度脑电图SR框架（Deep-EEGSR），该框架由一个紧凑的卷积网络和一个用于滤波器生成的辅助全连接网络（FGN）组成。 Deep-EEGSR 在编码 SR EEG 时应用图卷积来适应 EEG 通道之间的结构连接性。样本特定的动态卷积被设计为具有由FGN调整的滤波器参数，符合强烈的受试者个性的功能连接性。总体而言，Deep-EEGSR 在低分辨率 (LR) EEG 上运行，并通过端到端 SR 过程重建相应的 HR 采集。三个脑电图数据集（自闭症谱系障碍、情绪和运动想象）的实验结果表明：1）Deep-EEGSR 显着优于最先进的同类数据，归一化均方误差 (NMSE) 降低了 1% – 6%，信噪比 (SNR) 提高高达 1.2 dB，2) SR EEG 在 ASD 辨别和典型 ASD EEG 特征的空间定位方面表现出优于 LR 替代方案，并且这种优势甚至随着SR 的规模。