A novel transferability attention neural network model for EEG emotion recognition

Li, Yang; Fu, Boxun; Fu, Li; Shi, Guangming; Zheng, Wenming

doi:10.1016/j.neucom.2021.02.048

Cited by 55 publications

(20 citation statements)

References 24 publications

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“…SVM [24] 83.30/--DBN [30] 86.08/--SOGNN [31] 86.81/5.79 74.38/1.50 LDA [25] 90.93/--DGCNN [32] 90.40/8.48 -BiHDM [33] 93.12/6.06 -TANN [38] 93.34/6.64 -3DCNN-BiLSTM [27] 93.38/2.66 -4D_CRNN [35] 94.08/2.55 67.48/0.39 RGNN [51] 94.24/5.95 -DE-CNN-BiLSTM [26] 94.82/--DCCA [39] 95.08/ Referring to the baseline models on the SEED dataset, two baseline models 4D_CRNN [35] and SOGNN [31] that can be reproduced with the shared code were selected for comparison when validating on the LE-EEG dataset. Table 5 presents the comparison with the baseline models.…”

Section: Methods Seed Le-eegmentioning

confidence: 99%

“…Researchers in the field of EEG emotion recognition found that the attention mechanism is like the idea of focusing on emotion-related brain regions and started to try using this in the field of EEG emotion recognition to improve the model performance. For instance, Li et al proposed the transferable attention neural network (TANN) with 93.34% ACC and 6.64% STD, which used two directed RNN modules to extract features from whole brain regions and global attention layer fusion features to highlight the key brain regions for emotion classification [ 38 ].…”

Section: Related Workmentioning

confidence: 99%

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EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

Zhu

Rong

Zhao

et al. 2022

Sensors

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Understanding learners’ emotions can help optimize instruction sand further conduct effective learning interventions. Most existing studies on student emotion recognition are based on multiple manifestations of external behavior, which do not fully use physiological signals. In this context, on the one hand, a learning emotion EEG dataset (LE-EEG) is constructed, which captures physiological signals reflecting the emotions of boredom, neutrality, and engagement during learning; on the other hand, an EEG emotion classification network based on attention fusion (ECN-AF) is proposed. To be specific, on the basis of key frequency bands and channels selection, multi-channel band features are first extracted (using a multi-channel backbone network) and then fused (using attention units). In order to verify the performance, the proposed model is tested on an open-access dataset SEED (N = 15) and the self-collected dataset LE-EEG (N = 45), respectively. The experimental results using five-fold cross validation show the following: (i) on the SEED dataset, the highest accuracy of 96.45% is achieved by the proposed model, demonstrating a slight increase of 1.37% compared to the baseline models; and (ii) on the LE-EEG dataset, the highest accuracy of 95.87% is achieved, demonstrating a 21.49% increase compared to the baseline models.

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Section: Methods Seed Le-eegmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

Zhu

Rong

Zhao

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The neural network was used for training to distinguish emotional states, and the classification accuracy on the SEED dataset was 84.35%. Li et al (2021) proposed a kind of transferable attention neural network (TANN) for EEG emotion recognition. The network took into account the internal structure information of electrodes and adaptively highlights the data and samples of transferable EEG brain regions through local and global attention mechanisms to learn emotion recognition information.…”

Section: Emotion Recognition Based On Database For Emotion Analysis Using Physiological Signalsmentioning

confidence: 99%

Review on Emotion Recognition Based on Electroencephalography

Liu

Zhang

et al. 2021

Front. Comput. Neurosci.

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Emotions are closely related to human behavior, family, and society. Changes in emotions can cause differences in electroencephalography (EEG) signals, which show different emotional states and are not easy to disguise. EEG-based emotion recognition has been widely used in human-computer interaction, medical diagnosis, military, and other fields. In this paper, we describe the common steps of an emotion recognition algorithm based on EEG from data acquisition, preprocessing, feature extraction, feature selection to classifier. Then, we review the existing EEG-based emotional recognition methods, as well as assess their classification effect. This paper will help researchers quickly understand the basic theory of emotion recognition and provide references for the future development of EEG. Moreover, emotion is an important representation of safety psychology.

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“…Li Y. et al ( 2018 ) and Li et al ( 2020 ) proposed BiDANN and BiHDM networks for EEG emotion recognition, considering the asymmetry of emotion response between left and right hemispheres of human brain. Li et al ( 2021 ) proposed a Transferable Attention Neural Network (TANN), which considers local and global attention mechanism information for emotion recognition. In addition, some researchers considered the spatial information of EEG features, and arrange and distribute the features of each channel through the physical location before inputting them into the neural network.…”

Section: Introductionmentioning

confidence: 99%

Linking Multi-Layer Dynamical GCN With Style-Based Recalibration CNN for EEG-Based Emotion Recognition

Bao

Yang

et al. 2022

Front. Neurorobot.

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Electroencephalography (EEG)-based emotion computing has become one of the research hotspots of human-computer interaction (HCI). However, it is difficult to effectively learn the interactions between brain regions in emotional states by using traditional convolutional neural networks because there is information transmission between neurons, which constitutes the brain network structure. In this paper, we proposed a novel model combining graph convolutional network and convolutional neural network, namely MDGCN-SRCNN, aiming to fully extract features of channel connectivity in different receptive fields and deep layer abstract features to distinguish different emotions. Particularly, we add style-based recalibration module to CNN to extract deep layer features, which can better select features that are highly related to emotion. We conducted two individual experiments on SEED data set and SEED-IV data set, respectively, and the experiments proved the effectiveness of MDGCN-SRCNN model. The recognition accuracy on SEED and SEED-IV is 95.08 and 85.52%, respectively. Our model has better performance than other state-of-art methods. In addition, by visualizing the distribution of different layers features, we prove that the combination of shallow layer and deep layer features can effectively improve the recognition performance. Finally, we verified the important brain regions and the connection relationships between channels for emotion generation by analyzing the connection weights between channels after model learning.

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A novel transferability attention neural network model for EEG emotion recognition

Cited by 55 publications

References 24 publications

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

EEG Emotion Classification Network Based on Attention Fusion of Multi-Channel Band Features

Review on Emotion Recognition Based on Electroencephalography

Linking Multi-Layer Dynamical GCN With Style-Based Recalibration CNN for EEG-Based Emotion Recognition

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