Hierarchy Graph Convolution Network and Tree Classification for Epileptic Detection on Electroencephalography Signals

Zeng, Difei; Huang, Kejie; Xu, Cenglin; Shen, Haibin; Zhong, Chen

doi:10.1109/tcds.2020.3012278

Cited by 29 publications

(12 citation statements)

References 25 publications

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“…The adjacency matrix W

\in R^{n \times n}

of each mini‐epoch, representing the spatial correlation along with the channel signals

X \in R^{n \times M}

is one of the inputs of the ST‐GCN model shown in Figure 1a. In some studies on brain disorders based on GCN (Chen et al, 2020; Li, Liu, et al, 2021; Zeng et al, 2020; Zhang et al, 2021; Zhao et al, 2021), the relationship of different channels of EEG is short of effective prior guidance and the adjacency matrix cannot ensure the utilisation of the coupling information between each channel. To address these issues, we first apply functional connectivity, which has been proven to be useful in AD classification, to construct the adaptive adjacency matrix to extract spatial coupling features.…”

Section: Methodsmentioning

confidence: 99%

“…Building neural networks under the graph theory, graph convolutional neural networks (GCNs) have been developed specifically to handle highly multirelational graph data by jointly leveraging node‐specific sequential features and cross‐nodes topologically associative features in the graph domain (Gallicchio & Micheli, 2010 ; Gori et al, 2005 ; Scarselli et al, 2008 ; Sperduti & Starita, 1997 ). In recent 2 years, GCNs have been applied in the diagnoses of various brain disorders, such as children's ASD evaluation (Zhang et al, 2021 ), detection of epileptic (Zeng et al, 2020 ; Zhao et al, 2021 ), seizure prediction (Li, Liu, et al, 2021 ), and epilepsy classification (Chen et al, 2020 ). As far as we are concerned, there are no AD diagnostic approaches based on GCN‐related models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

Shan

Cao

Huo

et al. 2022

Human Brain Mapping

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Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial-temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG.

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“…The adjacency matrix W

\in R^{n \times n}

of each mini‐epoch, representing the spatial correlation along with the channel signals

X \in R^{n \times M}

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

Shan

Cao

Huo

et al. 2022

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…We overcome this challenge by learning the interdependency between the EEG electrodes as part of the training process. Recently, many graph based methods have been proposed for extracting spatio-temporal information from EEG signals [21,22,23,24]. However, most of these methods treat the graph network connections as static and do not learn the functional connectivities between the EEG electrodes as a part of the training process.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Graph Modeling Of Simultaneous EEG And Eye-Tracking Data For Reading Task Identification

Mathur

Mittal

Manocha

2021

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We present a new approach, that we call AdaGTCN, for identifying human reader intent from Electroencephalogram (EEG) and Eye movement (EM) data in order to help differentiate between normal reading and task-oriented reading. Understanding the physiological aspects of the reading process (the cognitive load and the reading intent) can help improve the quality of crowd-sourced annotated data. Our method, Adaptive Graph Temporal Convolution Network (AdaGTCN), uses an Adaptive Graph Learning Layer and Deep Neighborhood Graph Convolution Layer for identifying the reading activities using time-locked EEG sequences recorded during word-level eye-movement fixations. Adaptive Graph Learning Layer dynamically learns the spatial correlations between the EEG electrode signals while the Deep Neighborhood Graph Convolution Layer exploits temporal features from a dense graph neighborhood to establish the state of the art in reading task identification over other contemporary approaches. We compare our approach with several baselines to report an improvement of 6.29% on the ZuCo 2.0 dataset, along with extensive ablation experiments.

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“…However, these algorithms can not detect or extract complex EEG features, and they used to obtain general classification results. In recent years, deep learning methods have demonstrated its efficiency and flexibility for its excellent performance, especially Convolutional Neural Network (CNN) [18], Long Short-Term Memory (LSTM) [19], and Graph Neural Network (GNN) [20]. After intensive exploration, attention mechanism has been successfully adapted to EEG emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

JDAT: Joint-Dimension-Aware Transformer with Strong Flexibility for EEG Emotion Recognition

Wang¹,

Zhou²,

Shen³

et al. 2021

Preprint

Self Cite

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<div>Electroencephalography (EEG) emotion recognition, an important task in Human-Computer Interaction (HCI), has made a great breakthrough with the help of deep learning algorithms. Although the application of attention mechanism on conventional models has improved its performance, most previous research rarely focused on multiplex EEG features jointly, lacking a compact model with unified attention modules. This study proposes Joint-Dimension-Aware Transformer (JDAT), a robust model based on squeezed Multi-head Self-Attention (MSA) mechanism for EEG emotion recognition. The adaptive squeezed MSA applied on multidimensional features enables JDAT to focus on diverse EEG information, including space, frequency, and time. Under the joint attention, JDAT is sensitive to the complicated brain activities, such as signal activation, phase-intensity couplings, and resonance. Moreover, its gradually compressed structure contains no recurrent or parallel modules, greatly reducing the memory and complexity, and accelerating the inference process. The proposed JDAT is evaluated on DEAP, DREAMER, and SEED datasets, and experimental results show that it outperforms state-of-the-art methods along with stronger flexibility.</div>

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Hierarchy Graph Convolution Network and Tree Classification for Epileptic Detection on Electroencephalography Signals

Cited by 29 publications

References 25 publications

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

Dynamic Graph Modeling Of Simultaneous EEG And Eye-Tracking Data For Reading Task Identification

JDAT: Joint-Dimension-Aware Transformer with Strong Flexibility for EEG Emotion Recognition

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