A Domain Generative Graph Network for EEG-Based Emotion Recognition

Gu, Yun; Zhong, Xinyue; Qu, Cheng; Liu, Chuanjun; Chen, Bin

doi:10.1109/jbhi.2023.3242090

Cited by 15 publications

(2 citation statements)

References 50 publications

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“…Yun Gu et. al [72] presemted EEG model for the intelligence and humanization of brain-computer interface (BCI). [73] presented a review on Brain-Computer Interac-tion (BCI) system intelligence has become more dependent on electroencephalogram (EEG)-based emotion recognition.…”

Section: Machine Learning Techniques Used For Emotion Detectionmentioning

confidence: 99%

A Survey on Multi-modal Emotion Detection Techniques

Chatterjee,

Shah,

Bhatt

et al. 2024

Preprint

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The utilization of emotion detection and recognition technologies has revolution- ized human-computer interactions in various fields such as sentiment analysis, health monitoring, education, and automotive interfaces. Previously, traditional systems relied on single-channel affect sensing, which limited their ability to cap- ture the complexity of human emotions. However, humans naturally combine multiple cues such as facial expressions, speech, gestures, and contextual factors when expressing their emotions. As a result, there has been a growing inter- est in multi-modal emotion frameworks that integrate different sensory streams to obtain more comprehensive emotion assessments. These holistic perspectives allow for the capture of nuanced affective information that would otherwise be difficult to represent. In this survey paper, we delve into the latest advancements in emotion recognition systems, examining fusion techniques, feature engineer- ing methods, and classification architectures that leverage inputs from various modalities such as vision, audio, and text. Our focus is to showcase innova- tive interventions throughout the entire pipeline, from preprocessing raw signals to predicting emotion labels, in order to enable robust multi-modal analysis. Through detailed theoretical discussions and practical case studies, this paper aims to inspire further research by providing insights into the current state-of- the-art, highlighting open challenges, and exploring promising avenues in emotion detection through cross-modal learning.

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Section: Machine Learning Techniques Used For Emotion Detectionmentioning

confidence: 99%

A Survey on Multi-modal Emotion Detection Techniques

Chatterjee,

Shah,

Bhatt

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…It plays a vital role in correctly interpreting behavior and facilitating effective communication ( Jenke et al, 2014 ). In the development of brain-computer interface (BCI) systems, the urgency to empower machines with the ability to assist in analyzing human emotions is significant ( Gu et al, 2023 ). Consequently, emotion recognition has emerged as one of the crucial research directions in affective computing ( Hu et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism

Chen,

Liao,

Dai

et al. 2024

Front. Comput. Neurosci.

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EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI). Currently, most researches focus on improving accuracy, while neglecting further research on the interpretability of models, we are committed to analyzing the impact of different brain regions and signal frequency bands on emotion generation based on graph structure. Therefore, this paper proposes a method named Dual Attention Mechanism Graph Convolutional Neural Network (DAMGCN). Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features. Furthermore, we employ the self-attention mechanism of the Transformer model which allocates more electrode channel weights and signal frequency band weights to important brain regions and frequency bands. The visualization of attention mechanism clearly demonstrates the weight allocation learned by DAMGCN. During the performance evaluation of our model on the DEAP, SEED, and SEED-IV datasets, we achieved the best results on the SEED dataset, showing subject-dependent experiments’ accuracy of 99.42% and subject-independent experiments’ accuracy of 73.21%. The results are demonstrably superior to the accuracies of most existing models in the realm of EEG-based emotion recognition.

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Preserving node similarity adversarial learning graph representation with graph neural network

Yang,

Zhang,

et al. 2024

Engineering Reports

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In recent years, graph neural networks (GNNs) have showcased a strong ability to learn graph representations and have been widely used in various practical applications. However, many currently proposed GNN‐based representation learning methods do not retain neighbor‐based node similarity well, and this structural information is crucial in many cases. To address this issue, drawing inspiration from generative adversarial networks (GANs), we propose PNS‐AGNN (i.e., Preserving Node Similarity Adversarial Graph Neural Networks), a novel framework for acquiring graph representations, which can preserve neighbor‐based node similarity of the original graph and efficiently extract the nonlinear structural features of the graph. Specifically, we propose a new positive sample allocation strategy based on a node similarity index, where the generator can generate vector representations that satisfy node similarity through adversarial training. In addition, we also adopt an improved GNN as the discriminator, which utilizes the original graph structure for recursive neighborhood aggregation to maintain the local structure and feature information of nodes, thereby enhancing the graph representation's ability. Finally, we experimentally demonstrate that PNS‐AGNN significantly improves various tasks, including link prediction, node classification, and visualization.

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A Domain Generative Graph Network for EEG-Based Emotion Recognition

Cited by 15 publications

References 50 publications

A Survey on Multi-modal Emotion Detection Techniques

A Survey on Multi-modal Emotion Detection Techniques

EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism

Preserving node similarity adversarial learning graph representation with graph neural network

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