Multiview Feature Fusion Attention Convolutional Recurrent Neural Networks for EEG‐Based Emotion Recognition

Xin, Ruihao; Miao, Fengbo; Cong, Ping; Zhang, Fan; Xin, Yongxian; Feng, Xin

doi:10.1155/2023/9281230

Cited by 4 publications

(3 citation statements)

References 33 publications

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“…(8) Multi-aCRNN (Xin et al, 2023 [ 49 ]): The multi-aCRNN model proposes a multi-view feature fusion attention convolutional recurrent neural network approach. It combines CNN, GRU, and attention mechanisms while employing label smoothing to reduce noise interference.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

EEG Emotion Recognition by Fusion of Multi-Scale Features

Du,

Meng,

Qiu

et al. 2023

Brain Sciences

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Electroencephalogram (EEG) signals exhibit low amplitude, complex background noise, randomness, and significant inter-individual differences, which pose challenges in extracting sufficient features and can lead to information loss during the mapping process from low-dimensional feature matrices to high-dimensional ones in emotion recognition algorithms. In this paper, we propose a Multi-scale Deformable Convolutional Interacting Attention Network based on Residual Network (MDCNAResnet) for EEG-based emotion recognition. Firstly, we extract differential entropy features from different channels of EEG signals and construct a three-dimensional feature matrix based on the relative positions of electrode channels. Secondly, we utilize deformable convolution (DCN) to extract high-level abstract features by replacing standard convolution with deformable convolution, enhancing the modeling capability of the convolutional neural network for irregular targets. Then, we develop the Bottom-Up Feature Pyramid Network (BU-FPN) to extract multi-scale data features, enabling complementary information from different levels in the neural network, while optimizing the feature extraction process using Efficient Channel Attention (ECANet). Finally, we combine the MDCNAResnet with a Bidirectional Gated Recurrent Unit (BiGRU) to further capture the contextual semantic information of EEG signals. Experimental results on the DEAP dataset demonstrate the effectiveness of our approach, achieving accuracies of 98.63% and 98.89% for Valence and Arousal dimensions, respectively.

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Section: Experimental Results and Analysismentioning

confidence: 99%

EEG Emotion Recognition by Fusion of Multi-Scale Features

Du,

Meng,

Qiu

et al. 2023

Brain Sciences

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show abstract

“…Finally, the multi-head attention mechanism is used to learn the attention weights of node features to enhance the learning ability of the model. After being processed by the GAT attention layer, the features of node i can be expressed as equation (7).…”

Section: Sdc-gat Implementation Principlementioning

confidence: 99%

“…Deep neural networks have shown good results in the field of EEG emotion recognition. Convolutional Neural Network (CNN) is an important deep learning model [5][6][7][8]. It can comprehensively mine and fuse the representation information of samples and is applied to EEG emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

EEG Emotion Recognition Based on Self-Distillation Convolutional Graph Attention Network

Chao,

Feng

2024

EAI Endorsed Trans e-Learn

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A convolution graph attention model based on self-distillation convolutional graph attention network (SDC-GAT) is proposed for multi-channel EEG emotion recognition. Firstly, two-dimensional feature matrix based on EEG time-domain features are constructed, and the matrix is fed into the graph attention neural network to learn the internal connections between electrical brain channels located in different brain regions. Meanwhile, the three-dimensional feature matrix is constructed according to the relative positions of the electrode channels, and the self-distillation network is employed to extract local high-level abstract features containing electrode spatial position information from the three-dimensional feature matrix. Finally, outputs of the two networks are integrated to determine the emotional states. Experiments were performed on the DEAP dataset. The experimental results show that the spatial domain information of the electrode channel and the internal connection relationship between different channels are beneficial for emotion recognition. In addition, the proposed model can effectively fuse these information to improve the performance of multi-channel EEG emotion recognition.

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