Feature hypergraph representation learning on spatial-temporal correlations for EEG emotion recognition

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

doi:10.1007/s11571-022-09890-3

Cited by 10 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, Figure 5 shows the confusion matrix of Bi-ViTNet on the SEED datasets. The results show that for Bi-ViTNet, neutral emotions are easier to identify than negative emotions and positive emotions [22]. Table 3 shows the performance of all models on the SEED-IV dataset.…”

Section: E Results Analysis and Comparisonmentioning

confidence: 99%

“…Non-physiological signals, including speech, posture, and facial expression [21], are external manifestations of human emotions. Physiological signals, corresponding to the physiological reactions caused by emotions, such as eye electricity, ECG, EMG, and EEG, are human recessive emotional expressions [22]. Non-physiological signals, such as facial expressions and speech, are limited in their ability to reliably reflect an individual's true emotional state, as humans may conceal their emotions through masking their facial expression and voice.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bi-Branch Vision Transformer Network for EEG Emotion Recognition

Lü

Tan

2023

IEEE Access

View full text Add to dashboard Cite

Electroencephalogram (EEG) signals have emerged as an important tool for emotion research due to their objective reflection of real emotional states. Deep learning-based EEG emotion classification algorithms have made preliminary progress, but existing models struggle with capturing long-range dependence and integrating temporal, frequency, and spatial domain features to limit their classification ability. To address these challenges, this study proposes a Bi-branch Vision Transformerbased EEG emotion recognition model, Bi-ViTNet, that integrates spatial-temporal and spatial-frequency feature representations. Specifically, Bi-ViTNet is composed of spatial-frequency feature extraction branch and spatial-temporal feature extraction branch, which can fuse spatial-frequency-temporal features in a unified framework. Each branch is composed of Linear Embedding and Transformer Encoder, which is used to extract spatial-frequency features and spatial-temporal features. Finally, fusion and classification are performed by the Fusion and Classification layer. Experiments on SEED and SEED-IV datasets demonstrate that Bi-ViTNet outperforms state-of-the-art baselines.

show abstract

Section: E Results Analysis and Comparisonmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bi-Branch Vision Transformer Network for EEG Emotion Recognition

Lü

Tan

2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For instance, Zhang et al (2019) introduced a graph-based hierarchical model that classifies motor intentions based on the relationships between EEG signals and their spatial information. Li et al (2023) proposed a spatial-temporal hypergraph convolutional network (STHGCN) to capture higherorder relationships in EEG emotion recognition, achieved leading results on the SEED and SEED-IV datasets. Recently, Wagh and Varatharajah (2020) employed graph convolutional neural networks (GCNN) for the classification of epilepsy and normal data, achieving an AUC of 0.90.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the H, D v and D e generated during the hypergraph construction process, as well as the subject features input f Conv−LSTM ∈ R M×S×Num1 and f PSD ∈ R M×S×Num2 , where Num1 N × t represented the feature dimension of the spatiotemporal convolution branch and Num2 N × 6 represented the feature dimension of the PSD branch, hypergraph convolutions were conducted for each branch (Li et al, 2023), which were defined as Eq. ( 11).…”

Section: Hypergraph Convolutionmentioning

confidence: 99%

An epilepsy detection method based on multi-dimensional feature extraction and dual-branch hypergraph convolutional network

Liu,

Yang,

et al. 2024

Front. Physiol.

View full text Add to dashboard Cite

Epilepsy is a disease caused by abnormal neural discharge, which severely harms the health of patients. Its pathogenesis is complex and variable with various forms of seizures, leading to significant differences in epilepsy manifestations among different patients. The changes of brain network are strongly correlated with related pathologies. Therefore, it is crucial to effectively and deeply explore the intrinsic features of epilepsy signals to reveal the rules of epilepsy occurrence and achieve accurate detection. Existing methods have faced the following issues: 1) single approach for feature extraction, resulting in insufficient classification information due to the lack of rich dimensions in captured features; 2) inability to deeply analyze the essential commonality of epilepsy signal after feature extraction, making the model susceptible to data distribution and noise interference. Thus, we proposed a high-precision and robust model for epileptic seizure detection, which, for the first time, applies hypergraph convolution to the field of epilepsy detection. Through a hypergraph network structure constructed based on relationships between channels in electroencephalogram (EEG) signals, the model explores higher-order characteristics of epilepsy EEG data. Specifically, we use the Conv-LSTM module and Power spectral density (PSD), a two-branch parallel method, to extract channel features from space-time and frequency domains to solve the problem of insufficient feature extraction, and can adequately describe the data structure and distribution from multiple perspectives through double-branch parallel feature extraction. In addition, we construct a hypergraph on the captured features to explore the intrinsic features in the high-dimensional space in an attempt to reveal the essential commonality of epileptic signal feature extraction. Finally, using the ensemble learning concept, we accomplished epilepsy detection on the dual-branch hypergraph convolution. The model underwent leave-one-out cross-validation on the TUH dataset, achieving an average accuracy of 96.9%, F1 score of 97.3%, Pre of 98.2% and Re of 96.7%. In addition, the model was generalized performance tested on CHB-MIT scalp EEG dataset with leave-one-out cross-validation, and the average ACC, F1 score, Pre and Re were 94.4%, 95.1%, 95.8%, and 93.9% respectively. Experimental results indicate that the model outperforms related literature, providing valuable reference for the clinical application of epilepsy detection.

show abstract

“…Lu et al [19] proposed a time-frequency domain feature, which focuses on calculating the differential entropy (DE) feature at five different frequency band frequencies. Li et al [20] proposed a space-time hypergraph convolutional network to explore spatial and temporal correlations in specific emotional states, and found that spatial domain information can effectively improve the accuracy of emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Domain-Adaptive Emotion Recognition Based on Horizontal Vertical Flow Representation of EEG Signals

Bai

et al. 2023

IEEE Access

View full text Add to dashboard Cite

With the development of cognitive science and brain science, brain-computer interface technology can use Electroencephalogram (EEG) signals to better represent the inner changes of emotions. In this paper, A video-induced emotional stimulation experimental paradigm was designed, and the EEG signals of 15 hearing-impaired subjects under three emotions (positive, neutral, and negative) were collected. Considering the flow diffusion properties of EEG signals, we used the diffusion effect based on horizontal representation and vertical representation forms to obtain the spatial domain features. After EEG preprocessing, the differential entropy feature (DE) in the frequency domain is extracted. The frequency domain features of 62 channels are delivered to two Bi-directional Long Short-Term Memory (BiLSTM) to obtain spatial domain features of horizontal and vertical representations respectively, and then two kinds of domain features are fused by the residual network. The attention mechanism is applied to effectively extract emotional representational information from the fused features. To solve the cross-subject problem of emotion recognition, the domain adaptation method is utilized, and a center alignment loss function is applied to increase the distance of inter-class and reduce the distance of intra-class. According to the experimental results, the average accuracies of 75.89% (subject-dependent) and 69.59% (cross-subject) are obtained. Moreover, the validation was also performed on the public dataset SEED, achieving average accuracies of 93.99% (subject-dependent) and 84.22% (cross-subject), respectively.

show abstract

Feature hypergraph representation learning on spatial-temporal correlations for EEG emotion recognition

Cited by 10 publications

References 24 publications

Bi-Branch Vision Transformer Network for EEG Emotion Recognition

Bi-Branch Vision Transformer Network for EEG Emotion Recognition

An epilepsy detection method based on multi-dimensional feature extraction and dual-branch hypergraph convolutional network

Domain-Adaptive Emotion Recognition Based on Horizontal Vertical Flow Representation of EEG Signals

Contact Info

Product

Resources

About