LGGNet: Learning From Local-Global-Graph Representations for Brain–Computer Interface

Ding, Yi; Robinson, Neethu; Tong, Chengxuan; Zeng, Qiuhao; Guan, Cuntai

doi:10.1109/tnnls.2023.3236635

Cited by 28 publications

(11 citation statements)

References 59 publications

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“…To validate the efficacy of our proposed method, we conducted a comparative analysis with various existing methods on both public and our datasets, including ShallowConvNet [24], DeepConvNet [24], EEGNet [25], TSception [23], T-GCN [48], DCGRU [28], and LGGNet [45]. We first provide a brief overview of the similarities and dissimilarities between our proposed method and the other methods.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…DCGRU combines diffusion convolution and gated recurrent units and attains exceptional seizure detection and classification performance. Ding et al [45] introduced neuroscientific prior knowledge and defined three types of local-global graph structures, which were uti- We implement identical data preprocessing and crossvalidation strategies to those of other models and conduct experiments on two datasets. The results are reported in Table I and Table II, indicating that our proposed method outperforms other models in detecting depression in both datasets.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

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Exploring Adaptive Graph Topologies and Temporal Graph Networks for EEG-Based Depression Detection

Luo,

Rao,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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In recent years, Graph Neural Networks (GNNs) based on deep learning techniques have achieved promising results in EEG-based depression detection tasks but still have some limitations. Firstly, most existing GNNbased methods use pre-computed graph adjacency matrices, which ignore the differences in brain networks between individuals. Additionally, methods based on graphstructured data do not consider the temporal dependency information of brain networks. To address these issues, we propose a deep learning algorithm that explores adaptive graph topologies and temporal graph networks for EEGbased depression detection. Specifically, we designed an Adaptive Graph Topology Generation (AGTG) module that can adaptively model the real-time connectivity of the brain networks, revealing differences between individuals. In addition, we designed a Graph Convolutional Gated Recurrent Unit (GCGRU) module to capture the temporal dynamical changes of brain networks. To further explore the differential features between depressed and healthy individuals, we adopt Graph Topology-based Max-Pooling (GTMP) module to extract graph representation vectors accurately. We conduct a comparative analysis with several advanced algorithms on both public and our own datasets. The results reveal that our final model achieves the highest Area Under the Receiver Operating Characteristic Curve (AUROC) on both datasets, with values of 83% and 99%, respectively. Furthermore, we perform extensive validation experiments demonstrating our proposed method's effectiveness and advantages. Finally, we present a comprehensive discussion on the differences in brain networks between healthy and depressed individuals based on the outputs of our final model's AGTG and GTMP modules.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Comparison With Other Methodsmentioning

confidence: 99%

Exploring Adaptive Graph Topologies and Temporal Graph Networks for EEG-Based Depression Detection

Luo,

Rao,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

“…Due to the small sample size and inability to ensure adherence to a normal distribution, we choose the Paired Samples t-Test's non-parametric equivalent method, i.e. the Wilcoxon signed-rank test [52] for analysis, which was also employed in many related works [53][54][55] to prove the significance of model enhancement.…”

Section: Significance Testmentioning

confidence: 99%

Cross-modal credibility modelling for EEG-based multimodal emotion recognition

Zhang,

Liu,

Wang

et al. 2024

J. Neural Eng.

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Objective. The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities, and fusion credibility, which arises when one or multiple modalities fail to provide highly credible signals. Approach. In this paper, we introduce a novel multimodal physiological signal fusion model that incorporates both intra-inter modality reconstruction and sequential pattern consistency, thereby ensuring a computable and credible EEG-based multimodal emotion recognition. For the modality heterogeneity issue, we first implement a local self-attention transformer to obtain intra-modal features for each respective modality. Subsequently, we devise a pairwise cross-attention transformer to reveal the inter-modal correlations among different modalities, thereby rendering different modalities compatible and diminishing the heterogeneity concern. For the fusion credibility issue, we introduce the concept of sequential pattern consistency to measure whether different modalities evolve in a consistent way. Specifically, we propose to measure the varying trends of different modalities, and compute the inter-modality consistency scores to ascertain fusion credibility. Main results. We conduct extensive experiments on two benchmarked datasets (DEAP and MAHNOB-HCI) with the subject-dependent paradigm. For the DEAP dataset, our method improves the accuracy by 4.58%, and the F1 score by 0.63%, compared to the state-of-the-art baseline. Similarly, for the MAHNOB-HCI dataset, our method improves the accuracy by 3.97%, and the F1 score by 4.21%. In addition, we gain much insight into the proposed framework through significance test, ablation experiments, confusion matrices and hyperparameter analysis. Consequently, we demonstrate the effectiveness of the proposed credibility modelling through statistical analysis and carefully designed experiments. Significance. All experimental results demonstrate the effectiveness of our proposed architecture and indicate that credibility modelling is essential for multimodal emotion recognition.

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“…EEG signals can be naturally considered as graph-structured, with graph nodes representing EEG channels and graph edges representing spatial correlations among EEG channels. The dynamic DGCNN [29] and LGGNet [31] are two representative GNN variants for EEG analysis. Since LGGNet, which explores both local and global spatial correlations, is the most recently proposed and shown superior performance to an advanced version of DGCNN, we adopt LGGNet as the GNN family candidate operator.…”

Section: • Attention Family the Attention Mechanism Andmentioning

confidence: 99%

AutoEER: automatic EEG-based emotion recognition with neural architecture search

Liu

Zhang

et al. 2023

J. Neural Eng.

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Objective. Emotion recognition based on electroencephalography (EEG) is garnering increasing attention among researchers due to its wideranging applications and the rise of portable devices. Deep learning-based models have demonstrated impressive progress in EEG-based emotion recognition, thanks to their exceptional feature extraction capabilities. However, the manual design of deep networks is time-consuming and labour-intensive. Moreover, the inherent variability of EEG signals necessitates extensive customization of models, exacerbating these challenges. Neural architecture search (NAS) methods can alleviate the need for excessive manual involvement by automatically discovering the optimal network structure for EEG-based emotion recognition.Approach. In this regard, we propose AutoEER (Automatic EEG-based Emotion Recognition), a framework that leverages tailored NAS to automatically discover the optimal network structure for EEG-based emotion recognition. We carefully design a customized search space specifically for EEG signals, incorporating operators that effectively capture both temporal and spatial properties of EEG.Additionally, we employ a novel parameterization strategy to derive the optimal network structure from the proposed search space. Main results. Extensive experimentation on emotion classification tasks using two benchmark datasets, DEAP and SEED, has demonstrated that AutoEER outperforms state-of-the-art (SOTA) manual deep and NAS models. Specifically, compared to the optimal model WangNAS on the accuracy (ACC) metric, AutoEER improves its average accuracy on all datasets by 0.93%. Similarly, compared to the optimal model LiNAS on the F1 Ssore (F1) metric, AutoEER improves its average F1 score on all datasets by 4.51%. Furthermore, the architectures generated by AutoEER exhibit superior transferability compared to alternative methods. Significance.AutoEER represents a novel approach to EEG analysis, utilizing a specialized search space to design models tailored to individual subjects. This approach significantly reduces the labour and time costs associated with manual model construction in EEG research, holding great promise for advancing the field and streamlining research practices.

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LGGNet: Learning From Local-Global-Graph Representations for Brain–Computer Interface

Cited by 28 publications

References 59 publications

Exploring Adaptive Graph Topologies and Temporal Graph Networks for EEG-Based Depression Detection

Exploring Adaptive Graph Topologies and Temporal Graph Networks for EEG-Based Depression Detection

Cross-modal credibility modelling for EEG-based multimodal emotion recognition

AutoEER: automatic EEG-based emotion recognition with neural architecture search

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