Fusion Graph Representation of EEG for Emotion Recognition

Li, Menghang; Qiu, Min; Kong, Wanzeng; Zhu, Li; Ding, Yu

doi:10.3390/s23031404

Cited by 19 publications

(4 citation statements)

References 33 publications

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“…This clearly demonstrates the significant effect of using shorter time windows and focusing on the latest data during the emotion recognition process, showcasing the feasibility of real-time EEG emotion recognition. However, the experimental results are not as good as DGCNN [ 26 ], GLEM [ 27 ], BODF [ 28 ], and FGCN [ 29 ]. This may be due to other trade-offs made in achieving real-time performance, resulting in some data loss in other aspects.…”

Section: Resultsmentioning

confidence: 99%

Real-Time EEG-Based Emotion Recognition

Yu,

Li,

Zang

et al. 2023

Sensors

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Most studies have demonstrated that EEG can be applied to emotion recognition. In the process of EEG-based emotion recognition, real-time is an important feature. In this paper, the real-time problem of emotion recognition based on EEG is explained and analyzed. Secondly, the short time window length and attention mechanisms are designed on EEG signals to follow emotion change over time. Then, long short-term memory with the additive attention mechanism is used for emotion recognition, due to timely emotion updates, and the model is applied to the SEED and SEED-IV datasets to verify the feasibility of real-time emotion recognition. The results show that the model performs relatively well in terms of real-time performance, with accuracy rates of 85.40% and 74.26% on SEED and SEED-IV, but the accuracy rate has not reached the ideal state due to data labeling and other losses in the pursuit of real-time performance.

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Section: Resultsmentioning

confidence: 99%

Real-Time EEG-Based Emotion Recognition

Yu,

Li,

Zang

et al. 2023

Sensors

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“…In reference [46], Li et al propose a fusion graph convolutional network (FGCN) architecture for extracting and combining various relationships in EEG data to obtain a more comprehensive representation for emotion recognition. FGCN initially identifies brain connectivity features based on topology, causality, and function.…”

Section: Related Studiesmentioning

confidence: 99%

Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

Bagherzadeh,

Shalbaf,

Shoeibi

et al. 2024

IEEE Access

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The objective of this paper is to develop a novel emotion recognition system from electroencephalogram (EEG) signals using effective connectivity and deep learning methods. Emotion recognition is an important task for various applications such as human-computer interaction and, mental health diagnosis. The paper aims to improve the accuracy and robustness of emotion recognition by combining different effective connectivity (EC) methods and pre-trained convolutional neural networks (CNNs), as well as long short-term memory (LSTM). EC methods measure information flow in the brain during emotional states using EEG signals. We used three EC methods: transfer entropy (TE), partial directed coherence (PDC), and direct directed transfer function (dDTF). We estimated a fused image from these methods for each five-second window of 32-channel EEG signals. Then, we applied six pre-trained CNNs to classify the images into four emotion classes based on the two-dimensional valence-arousal model. We used the leave-one-subject-out crossvalidation strategy to evaluate the classification results. We also used an ensemble model to select the best results from the best pre-trained CNNs using the majority voting approach. Moreover, we combined the CNNs with LSTM to improve recognition performance. We achieved the average accuracy and F-score of 98.76%, 98.86%, 98.66 and 98.88% for classifying emotions using DEAP and MAHNOB-HCI datasets, respectively. Our results show that fused images can increase the accuracy and that an ensemble and combination of pretrained CNNs and LSTM can achieve high accuracy for automated emotion recognition. Our model outperformed other state-of-the-art systems using the same datasets for four-class emotion classification.

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“…With the aid of X l 1 and X l 2 , we undertake the computation of the adjacency matrix, which serves to represent the connectivity between various EEG channels or nodes within a graph-based brain network. Here, we have opted for four commonly employed functional connectivity methods, namely correlation (Cor), coherence (Coh), phase locking value (PLV), and phase lag index (PLI), all of which have shown promising results in EEG-based emotion recognition [48][49][50][51].…”

Section: Task-specific Adjacency Matrices Constructionmentioning

confidence: 99%

TSANN-TG: Temporal–Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition

Jiang,

Dai,

Ding

et al. 2024

Brain Sciences

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Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the realm of affective brain–computer interfaces. In this paper, we propose TSANN-TG (temporal–spatial attention neural network with a task-specific graph), a novel neural network architecture tailored for enhancing feature extraction and effectively integrating temporal–spatial features. TSANN-TG comprises three primary components: a node-feature-encoding-and-adjacency-matrices-construction block, a graph-aggregation block, and a graph-feature-fusion-and-classification block. Leveraging the distinct temporal scales of features from EEG signals, TSANN-TG incorporates attention mechanisms for efficient feature extraction. By constructing task-specific adjacency matrices, the graph convolutional network with an attention mechanism captures the dynamic changes in dependency information between EEG channels. Additionally, TSANN-TG emphasizes feature integration at multiple levels, leading to improved performance in emotion-recognition tasks. Our proposed TSANN-TG is applied to both our FTEHD dataset and the publicly available DEAP dataset. Comparative experiments and ablation studies highlight the excellent recognition results achieved. Compared to the baseline algorithms, TSANN-TG demonstrates significant enhancements in accuracy and F1 score on the two benchmark datasets for four types of cognitive tasks. These results underscore the significant potential of the TSANN-TG method to advance EEG-based emotion recognition.

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Fusion Graph Representation of EEG for Emotion Recognition

Cited by 19 publications

References 33 publications

Real-Time EEG-Based Emotion Recognition

Real-Time EEG-Based Emotion Recognition

Developing an EEG-Based Emotion Recognition Using Ensemble Deep Learning Methods and Fusion of Brain Effective Connectivity Maps

TSANN-TG: Temporal–Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition

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