脑电图（EEG）是一种有用的方法，可以在多媒体消费期间隐式监控用户感知状态。基于EEG的监测的实际使用的主要挑战之一是在脑电图分类中实现令人满意的准确性。不同脑区之间的连接是脑电图分类的重要属性。但是，如何定义给定任务的连接结构仍然是一个打开问题，因为没有关于连接结构应该如何最大化分类性能的实践。在本文中，我们提出了一种基于EEG的情绪视频分类的端到端神经网络模型，其可以直接从一组RAW EEG信号提取适当的多层图形结构和信号特征，并使用它们执行分类。实验结果表明，与使用手动定义的连接结构和信号特征的现有方法相比，我们的方法能够提高性能。此外，我们表明，在一致性方面，图形结构提取过程可靠，并且在大脑中发生的情绪感知的角度来看，学习的图形结构具有很大的意义。

Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within and among different functional areas of the brain, we propose LGGNet, a novel neurologically inspired graph neural network, to learn local-global-graph representations of electroencephalography (EEG) for Brain-Computer Interface (BCI). The input layer of LGGNet comprises a series of temporal convolutions with multi-scale 1D convolutional kernels and kernel-level attentive fusion. It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers. Using a defined neurophysiologically meaningful set of local and global graphs, LGGNet models the complex relations within and among functional areas of the brain. Under the robust nested cross-validation settings, the proposed method is evaluated on three publicly available datasets for four types of cognitive classification tasks, namely, the attention, fatigue, emotion, and preference classification tasks. LGGNet is compared with state-of-the-art methods, such as DeepConvNet, EEGNet, R2G-STNN, TSception, RGNN, AMCNN-DGCN, HRNN and GraphNet. The results show that LGGNet outperforms these methods, and the improvements are statistically significant (p<0.05) in most cases. The results show that bringing neuroscience prior knowledge into neural network design yields an improvement of classification performance. The source code can be found at https://github.com/yi-ding-cs/LGG

情感识别技术使计算机能够将人类情感状态分类为离散类别。但是，即使在短时间内，情绪也可能波动，而不是保持稳定状态。由于其3-D拓扑结构，也很难全面使用EEG空间分布。为了解决上述问题，我们在本研究中提出了一个本地时间空间模式学习图表网络（LTS-GAT）。在LTS-GAT中，使用划分和串扰方案来检查基于图形注意机制的脑电图模式的时间和空间维度的局部信息。添加了动力域歧视器，以提高针对脑电图统计数据的个体间变化的鲁棒性，以学习不同参与者的鲁棒性脑电图特征表示。我们在两个公共数据集上评估了LTS-GAT，用于在个人依赖和独立范式下进行情感计算研究。与其他现有主流方法相比，LTS-GAT模型的有效性被证明。此外，使用可视化方法来说明不同大脑区域和情绪识别的关系。同时，还对不同时间段的权重进行了可视化，以研究情绪稀疏问题。

Sleep stage recognition is crucial for assessing sleep and diagnosing chronic diseases. Deep learning models, such as Convolutional Neural Networks and Recurrent Neural Networks, are trained using grid data as input, making them not capable of learning relationships in non-Euclidean spaces. Graph-based deep models have been developed to address this issue when investigating the external relationship of electrode signals across different brain regions. However, the models cannot solve problems related to the internal relationships between segments of electrode signals within a specific brain region. In this study, we propose a Pearson correlation-based graph attention network, called PearNet, as a solution to this problem. Graph nodes are generated based on the spatial-temporal features extracted by a hierarchical feature extraction method, and then the graph structure is learned adaptively to build node connections. Based on our experiments on the Sleep-EDF-20 and Sleep-EDF-78 datasets, PearNet performs better than the state-of-the-art baselines.

基于脑电图（EEG）的脑生物识别技术已被越来越多地用于个人鉴定。传统的机器学习技术以及现代的深度学习方法已采用有希望的结果。在本文中，我们提出了EEG-BBNET，这是一个混合网络，该网络将卷积神经网络（CNN）与图形卷积神经网络（GCNN）集成在一起。 CNN在自动特征提取方面的好处以及GCNN通过图形表示在EEG电极之间学习连通性的能力被共同利用。我们检查了各种连通性度量，即欧几里得距离，皮尔逊的相关系数，相锁定值，相位滞后指数和RHO索引。在由各种脑部计算机界面（BCI）任务组成的基准数据集上评估了所提出的方法的性能，并将其与其他最先进的方法进行了比较。我们发现，使用会议内数据的平均正确识别率最高99.26％，我们的模型在事件相关电位（ERP）任务中的所有基线都优于所有基准。具有Pearson相关性和RHO指数的EEG-BBNET提供了最佳的分类结果。此外，我们的模型使用会议间和任务数据显示出更大的适应性。我们还研究了我们提出的模型的实用性，该模型的电极数量较少。额叶区域上的电极放置似乎最合适，性能损失最少。

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.

过去几十年来看，越来越多地采用的非侵入性神经影像学技术越来越大的进步，以检查人脑发展。然而，这些改进并不一定是更复杂的数据分析措施，能够解释功能性大脑发育的机制。例如，从单变量（大脑中的单个区域）转变为多变量（大脑中的多个区域）分析范式具有重要意义，因为它允许调查不同脑区之间的相互作用。然而，尽管对发育大脑区域之间的相互作用进行了多变量分析，但应用了人工智能（AI）技术，使分析不可解释。本文的目的是了解电流最先进的AI技术可以通知功能性大脑发展的程度。此外，还审查了哪种AI技术基于由发育认知神经科学（DCN）框架所定义的大脑发展的过程来解释他们的学习。这项工作还提出说明可解释的AI（Xai）可以提供可行的方法来调查功能性大脑发育，如DCN框架的假设。

神经科学领域的研究揭示了情绪模式和脑功能区域之间的关系，展示了不同脑区之间的动态关系是影响通过脑电图（EEG）确定的情绪识别的必要因素。此外，在脑电情绪识别中，我们可以观察到，基于相同的脑电图数据，我们可以观察到粗粒情绪之间的粗粒情绪之间的边界;这表明大型粗糙和小细粒度情绪变化的同意。因此，来自粗糙到细粒度类别的渐进分类过程可能有助于EEG情绪识别。因此，在本研究中，我们提出了一种逐步的图表卷积网络（PGCN），用于捕获EEG情绪信号中的这种固有特性，并逐步学习鉴别性EEG特征。为了适应不同的EEG模式，我们构建了一个双图模块，以表征不同EEG通道之间的内在关系，其中包含神经科学研究的动态功能连接和脑区的静态空间接近信息。此外，通过观察粗糙和细粒度的情绪之间的关系，我们采用双头模块，使PGCN能够逐步了解更多辨别性EEG特征，从粗粒（简单）到细粒度的类别（困难），参考情绪的分层特征。为了验证我们模型的性能，在两个公共数据集中进行了广泛的实验：种子-46和多模态生理情绪数据库（MPED）。

Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area. To keep up with the pace of new research, proper experimental design, fair evaluation, and independent benchmarks are essential. Design of strong baselines is an indispensable element of such works. In this thesis, we explore multiple approaches to graph classification. We focus on Graph Neural Networks (GNNs), which emerged as a de facto standard deep learning technique for graph representation learning. Classical approaches, such as graph descriptors and molecular fingerprints, are also addressed. We design fair evaluation experimental protocol and choose proper datasets collection. This allows us to perform numerous experiments and rigorously analyze modern approaches. We arrive to many conclusions, which shed new light on performance and quality of novel algorithms. We investigate application of Jumping Knowledge GNN architecture to graph classification, which proves to be an efficient tool for improving base graph neural network architectures. Multiple improvements to baseline models are also proposed and experimentally verified, which constitutes an important contribution to the field of fair model comparison.

Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic study of how to design effective GNNs for brain network analysis. To bridge this gap, we present BrainGB, a benchmark for brain network analysis with GNNs. BrainGB standardizes the process by (1) summarizing brain network construction pipelines for both functional and structural neuroimaging modalities and (2) modularizing the implementation of GNN designs. We conduct extensive experiments on datasets across cohorts and modalities and recommend a set of general recipes for effective GNN designs on brain networks. To support open and reproducible research on GNN-based brain network analysis, we host the BrainGB website at https://braingb.us with models, tutorials, examples, as well as an out-of-box Python package. We hope that this work will provide useful empirical evidence and offer insights for future research in this novel and promising direction.

认识到人类的感情在日常沟通中发挥着关键作用。神经科学已经证明，不同的情绪状态存在于不同脑区，脑电图频带和颞戳中不同程度的激活。在本文中，我们提出了一种新颖的结构来探索情感认可的信息脑电图。所提出的模块，由PST-Integn表示，由位置，光谱和颞件注意力模块组成，用于探索更多辨别性EEG特征。具体地，位置注意模块是捕获在空间尺寸中的不同情绪刺激的激活区域。光谱和时间注意力模块分别分配不同频带和时间片的权重。我们的方法是自适应的，也可以符合其作为插入式模块的3D卷积神经网络（3D-CNN）。我们在两个现实世界数据集进行实验。 3D-CNN结合我们的模块实现了有希望的结果，并证明了PST-关注能够从脑电图中捕获稳定的情感识别模式。

Graph neural networks (GNNs) have been successfully applied to early mild cognitive impairment (EMCI) detection, with the usage of elaborately designed features constructed from blood oxygen level-dependent (BOLD) time series. However, few works explored the feasibility of using BOLD signals directly as features. Meanwhile, existing GNN-based methods primarily rely on hand-crafted explicit brain topology as the adjacency matrix, which is not optimal and ignores the implicit topological organization of the brain. In this paper, we propose a spatial temporal graph convolutional network with a novel graph structure self-learning mechanism for EMCI detection. The proposed spatial temporal graph convolution block directly exploits BOLD time series as input features, which provides an interesting view for rsfMRI-based preclinical AD diagnosis. Moreover, our model can adaptively learn the optimal topological structure and refine edge weights with the graph structure self-learning mechanism. Results on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database show that our method outperforms state-of-the-art approaches. Biomarkers consistent with previous studies can be extracted from the model, proving the reliable interpretability of our method.

图表神经网络（GNNS）最近在人工智能（AI）领域的普及，这是由于它们作为输入数据相对非结构化数据类型的独特能力。尽管GNN架构的一些元素在概念上类似于传统神经网络（以及神经网络变体）的操作中，但是其他元件代表了传统深度学习技术的偏离。本教程通过整理和呈现有关GNN最常见和性能变种的动机，概念，数学和应用的细节，将GNN的权力和新颖性暴露给AI从业者。重要的是，我们简明扼要地向实际示例提出了本教程，从而为GNN的主题提供了实用和可访问的教程。

在大脑中找到适当的动态活动的适当表示对于许多下游应用至关重要。由于其高度动态的性质，暂时平均fMRI（功能磁共振成像）只能提供狭窄的脑活动视图。以前的作品缺乏学习和解释大脑体系结构中潜在动态的能力。本文构建了一个有效的图形神经网络模型，该模型均包含了从DWI（扩散加权成像）获得的区域映射的fMRI序列和结构连接性作为输入。我们通过学习样品水平的自适应邻接矩阵并进行新型多分辨率内群平滑来发现潜在大脑动力学的良好表示。我们还将输入归因于具有集成梯度的输入，这使我们能够针对每个任务推断（1）高度涉及的大脑连接和子网络，（2）成像序列的时间键帧，这些成像序列表征了任务，以及（3）歧视单个主体的子网络。这种识别特征在异质任务和个人中表征信号状态的关键子网的能力对神经科学和其他科学领域至关重要。广泛的实验和消融研究表明，我们提出的方法在空间 - 周期性图信号建模中的优越性和效率，具有对脑动力学的深刻解释。

衡量心理工作量的主要原因是量化执行任务以预测人类绩效的认知成本。不幸的是，一种评估具有一般适用性的心理工作量的方法。这项研究提出了一种新型的自我监督方法，用于从脑电图数据中使用深度学习和持续的大脑率，即认知激活的指标，而无需人类声明性知识，从而从脑电图数据进行了精神负荷建模。该方法是可培训的卷积复发性神经网络，该神经网络可通过空间保留脑电图数据的光谱地形图训练，以适合大脑速率变量。发现证明了卷积层从脑电图数据中学习有意义的高级表示的能力，因为受试者内模型的测试平均绝对百分比误差平均为11％。尽管确实提高了其准确性，但增加了用于处理高级表示序列的长期期内存储层并不重要。发现指出，认知激活的高级高水平表示存在准稳定的块，因为它们可以通过卷积诱导，并且似乎随着时间的流逝而彼此依赖，从而直观地与大脑反应的非平稳性质相匹配。跨主体模型，从越来越多的参与者的数据诱导，因此包含更多的可变性，获得了与受试者内模型相似的精度。这突出了人们在人们之间诱发的高级表示的潜在普遍性，这表明存在非依赖于受试者的认知激活模式。这项研究通过为学者提供一种用于心理工作负载建模的新型计算方法来促进知识的体系，该方法旨在通常适用，不依赖于支持可复制性和可复制性的临时人工制作的模型。

Neuroomaging的最新进展以及网络数据统计学习中的算法创新提供了一种独特的途径，可以集成大脑结构和功能，从而有助于揭示系统水平的一些大脑组织原则。在此方向上，我们通过曲线图编码器 - 解码器系统制定了一种模拟脑结构连接（SC）和功能连接（FC）之间的关系的监督图形表示学习框架，其中SC用作预测经验FC的输入。训练图卷积编码器捕获模拟实际神经通信的大脑区域之间的直接和间接相互作用，以及集成结构网络拓扑和节点（即，区域特定的）属性的信息。编码器学习节点级SC嵌入，它们组合以生成用于重建经验FC网络的（全大脑）图级表示。所提出的端到端模型利用多目标损失函数来共同重建FC网络，并学习用于下游主题的SC-To-Fc映射的判别图表表示（即，图形级）分类。综合实验表明，所述关系的学习表现从受试者的脑网络的内在属性中捕获有价值的信息，并导致提高对来自人类连接项目的大量重型饮酒者和非饮酒者的准确性提高。我们的工作提供了关于脑网络之间关系的新见解，支持使用图形表示学习的有希望的前景，了解有关人脑活动和功能的更多信息。

The classification of sleep stages plays a crucial role in understanding and diagnosing sleep pathophysiology. Sleep stage scoring relies heavily on visual inspection by an expert that is time consuming and subjective procedure. Recently, deep learning neural network approaches have been leveraged to develop a generalized automated sleep staging and account for shifts in distributions that may be caused by inherent inter/intra-subject variability, heterogeneity across datasets, and different recording environments. However, these networks ignore the connections among brain regions, and disregard the sequential connections between temporally adjacent sleep epochs. To address these issues, this work proposes an adaptive product graph learning-based graph convolutional network, named ProductGraphSleepNet, for learning joint spatio-temporal graphs along with a bidirectional gated recurrent unit and a modified graph attention network to capture the attentive dynamics of sleep stage transitions. Evaluation on two public databases: the Montreal Archive of Sleep Studies (MASS) SS3; and the SleepEDF, which contain full night polysomnography recordings of 62 and 20 healthy subjects, respectively, demonstrates performance comparable to the state-of-the-art (Accuracy: 0.867;0.838, F1-score: 0.818;0.774 and Kappa: 0.802;0.775, on each database respectively). More importantly, the proposed network makes it possible for clinicians to comprehend and interpret the learned connectivity graphs for sleep stages.

机器学习，在深入学习的进步，在过去分析时间序列方面表现出巨大的潜力。但是，在许多情况下，可以通过将其结合到学习方法中可能改善预测的附加信息。这对于由例如例如传感器位置的传感器网络而产生的数据至关重要。然后，可以通过通过图形结构建模，以及顺序（时间）信息来利用这种空间信息。适应深度学习的最新进展在各种图形相关任务中表明了有希望的潜力。但是，这些方法尚未在很大程度上适用于时间序列相关任务。具体而言，大多数尝试基本上围绕空间 - 时间图形神经网络巩固了时间序列预测的小序列长度。通常，这些架构不适合包含大数据序列的回归或分类任务。因此，在这项工作中，我们使用图形神经网络的好处提出了一种能够在多变量时间序列回归任务中处理这些长序列的架构。我们的模型在包含地震波形的两个地震数据集上进行测试，其中目标是预测在一组站的地面摇动的强度测量。我们的研究结果表明了我们的方法的有希望的结果，这是深入讨论的额外消融研究。

在神经科学领域，脑活动分析总是被认为是一个重要领域。精神分裂症（SZ）是一种严重影响世界各地人民的思想，行为和情感的大脑障碍。在Sz检测中被证明是一种有效的生物标志物的脑电图（EEG）。由于其非线性结构，EEG是非线性时间序列信号，并利用其进行调查，这是对其的影响。本文旨在利用深层学习方法提高基于EEG基于SZ检测的性能。已经提出了一种新的混合深度学习模型（精神分裂症混合神经网络），已经提出了卷积神经网络（CNN）和长短期存储器（LSTM）的组合。 CNN网络用于本地特征提取，LSTM已用于分类。所提出的模型仅与CNN，仅限LSTM和基于机器学习的模型进行了比较。已经在两个不同的数据集上进行了评估所有模型，其中数据集1由19个科目和数据集2组成，由16个科目组成。使用不同频带上的各种参数设置并在头皮上使用不同的电极组来进行几个实验。基于所有实验，显然提出的混合模型（SZHNN）与其他现有型号相比，拟议的混合模型（SZHNN）提供了99.9％的最高分类精度。该建议的模型克服了不同频带的影响，甚至没有5个电极显示出91％的更好的精度。该拟议的模型也在智能医疗保健和远程监控应用程序的医疗器互联网上进行评估。

Seizure type identification is essential for the treatment and management of epileptic patients. However, it is a difficult process known to be time consuming and labor intensive. Automated diagnosis systems, with the advancement of machine learning algorithms, have the potential to accelerate the classification process, alert patients, and support physicians in making quick and accurate decisions. In this paper, we present a novel multi-path seizure-type classification deep learning network (MP-SeizNet), consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory neural network (Bi-LSTM) with an attention mechanism. The objective of this study was to classify specific types of seizures, including complex partial, simple partial, absence, tonic, and tonic-clonic seizures, using only electroencephalogram (EEG) data. The EEG data is fed to our proposed model in two different representations. The CNN was fed with wavelet-based features extracted from the EEG signals, while the Bi-LSTM was fed with raw EEG signals to let our MP-SeizNet jointly learns from different representations of seizure data for more accurate information learning. The proposed MP-SeizNet was evaluated using the largest available EEG epilepsy database, the Temple University Hospital EEG Seizure Corpus, TUSZ v1.5.2. We evaluated our proposed model across different patient data using three-fold cross-validation and across seizure data using five-fold cross-validation, achieving F1 scores of 87.6% and 98.1%, respectively.