A Dual-Branch Dynamic Graph Convolution Based Adaptive TransFormer Feature Fusion Network for EEG Emotion Recognition

Sun, Mingyi; Cui, Weigang; Han, Hongbin; Hu, Bin; Li, Yang

doi:10.1109/taffc.2022.3199075

Cited by 51 publications

(17 citation statements)

References 36 publications

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“…Despite most of the surveyed papers being relatively recent, a wide range of GNN-based methods has already been proposed to classify EEG signals in a diverse set of tasks, such as emotion recognition, brain-computer interfaces, and psychological and neurodegenerative disorders and diseases [46], [53], [54], [56], [58], [61], [70], [72], [75], [83], [89], [106] Chebyshev Graph Convolution ✗ ✓ ✗ [49], [51], [55], [57], [59], [66], [67], [69], [71], [74], [76]- [78], [80], [82], [85], [90], [97], [99], [104] Graph Attention Network ✓ ✗ ✗ [60], [62], [73], [84], [88], [94], [98] This survey categorises the proposed GNN models in terms of their inputs and modules. Specifically, these are brain graph structure, node features and their preprocessing, GCN layers, node pooling mechanisms, and formation of graph embeddings.…”

Section: Discussionmentioning

confidence: 99%

“…These methods can be generally categorised as learnable or pre-defined. Multiple/Combined graph definitions -- [47], [49], [53], [54], [57]- [59], [61]- [64], [67], [69], [72], [79], [79], [81], [82], [87], [92], [102] [51], [53], [55], [57], [71], [72], [75], [78], [81], [82], [87], [89], [90], [92], [93], [93], [95]- [99], [101], [102] Raw signal ✓ ✗ ✗…”

Section: Definition Of Brain Graph Structurementioning

confidence: 99%

“…Alternatively, researchers explored various neural network models to obtain graph embeddings, such as CNN [52], [69], [78], (bi-)LSTM [51], [83], [84], [99], [100], transformer [89] and capsule networks [73]. Additionally, graph pooling methods, such as DiffPool [109], SAGPool [110], iPool [111], TAP [112] and HierCorrPool [113] can be used for this purpose.…”

Section: From Node Embeddings To Graph Embeddingmentioning

confidence: 99%

“…Instead, we suggest a comparative experimental study to be better suited to explore the various explainability options of GNN explainability. However, to maintain the comprehensiveness of this survey, we list the papers that report the use of certain methods of model explainability: [24], [50], [55], [89], [105], [106].…”

Section: Limitations Of Our Surveymentioning

confidence: 99%

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Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.

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Section: Discussionmentioning

confidence: 99%

Section: Definition Of Brain Graph Structurementioning

confidence: 99%

Section: From Node Embeddings To Graph Embeddingmentioning

confidence: 99%

Section: Limitations Of Our Surveymentioning

confidence: 99%

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Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Moreover, to further uncover the relationships between EEG channels, dynamic graph convolution has gained significant traction [16]. Dynamic graph neural networks employ a learnable adjacency matrix as a parameter, which is updated during the training process [17,18,19].…”

Section: Dynamic Graph Convolutional Network For Bcismentioning

confidence: 99%

EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training

Fu,

Li,

et al. 2023

cai

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Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient's brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient's overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography

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Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

Cong,

Wang,

Zhou

et al. 2024

Brain-X

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This comprehensive review aims to clarify the growing impact of Transformer‐based models in the fields of neuroscience, neurology, and psychiatry. Originally developed as a solution for analyzing sequential data, the Transformer architecture has evolved to effectively capture complex spatiotemporal relationships and long‐range dependencies that are common in biomedical data. Its adaptability and effectiveness in deciphering intricate patterns within medical studies have established it as a key tool in advancing our understanding of neural functions and disorders, representing a significant departure from traditional computational methods. The review begins by introducing the structure and principles of Transformer architectures. It then explores their applicability, ranging from disease diagnosis and prognosis to the evaluation of cognitive processes and neural decoding. The specific design modifications tailored for these applications and their subsequent impact on performance are also discussed. We conclude by providing a comprehensive assessment of recent advancements, prevailing challenges, and future directions, highlighting the shift in neuroscientific research and clinical practice towards an artificial intelligence‐centric paradigm, particularly given the prominence of Transformer architecture in the most successful large pre‐trained models. This review serves as an informative reference for researchers, clinicians, and professionals who are interested in understanding and harnessing the transformative potential of Transformer‐based models in neuroscience, neurology, and psychiatry.

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A Dual-Branch Dynamic Graph Convolution Based Adaptive TransFormer Feature Fusion Network for EEG Emotion Recognition

Cited by 51 publications

References 36 publications

Graph Neural Network-Based EEG Classification: A Survey

Graph Neural Network-Based EEG Classification: A Survey

EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

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