EEG Emotion Recognition Using Dynamical Graph Convolutional Neural Networks | Semantic Scholar (2024)

A multichannel EEG emotion recognition method based on phase-locking value (PLV) graph convolutional neural networks (P-GCNN) is proposed, which uses the PLV connectivity of EEG signals to determine the mode of emotional-related functional connectivity, which is used to represent the intrinsic relationship between EEG channels in different emotional states.

This article proposes a novel method to generate continuous images from discrete EEG signals by introducing offset variables following a Gaussian distribution for each EEG channel to alleviate the biased electrode coordinates during image generation.

A spatial-temporal feature fused convolutional graph attention network (STFCGAT) model based on multi-channel EEG signals for human emotion recognition that achieved state-of-the-art performance on cross-subject emotion recognition tasks for both datasets.

The graph convolutional neural network is used to analyze the emotion of EEG signal and the accuracy of the proposed method in the valence and arousal dimension are 83.23% and 85.82%, respectively.

This work proposes a brain network representation learning method that employs self-attention dynamic graph neural networks to obtain the spatial structure information and temporal evolution characteristics of brain networks.

This paper designed a novel architecture, i.e., broad dynamical graph learning system (BDGLS), to deal with EEG signals, which has the ability of extracting features on non-Euclidean domain and randomly generating nodes to find the desired connection weights simultaneously.

A regularized graph neural network for EEG-based emotion recognition that considers the biological topology among different brain regions to capture both local and global relations among different EEG channels and ablation studies show that the proposed adjacency matrix and two regularizers contribute consistent and significant gain to the performance of the model.

Experimental results show that the proposed functional connectivity and spatial-frequency feature graph neural networks (FCSF-GNN) achieves better recognition performance than the state-of-the-art methods in the DEAP dataset.

A novel Adaptive Hierarchical Graph Convolutional Network (AHGCN) is proposed, which includes the basic channel- level graph of EEG channels and the region-level graph of brain regions and an adaptive pooling operation to automatically partition brain regions rather than manually define them is proposed.

A multimodal emotion recognition method based on the attention recurrent graph convolutional neural network, which is represented by Mul-AT-RGCN, which performs feature learning in three dimensions of time, space, and frequency by excavating the complementary relationship of different modal data so that the learned deep emotion-related features are more discriminative.

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A novel regression model, called graph regularized sparse linear discriminant analysis (GraphSLDA), is proposed to deal with EEG emotion recognition problem and experimental results show that the proposed algorithm GraphSLDA is superior to the classic baselines.

Detailed experiments on EEG-based emotion recognition based on the SJTU emotion EEG dataset and experimental results demonstrate that the proposed GSCCA method would outperform the state-of-the-art EEG- based emotion recognition approaches.

The experimental results show that the DBN and DBN-HMM models improve the accuracy of EEG-based emotion classification in comparison with the state-of-the-art methods.

A novel emotion evocation and EEG-based feature extraction technique is presented, in which the mirror neuron system concept was adapted to efficiently foster emotion induction by the process of imitation, justifying the efficiency of the proposed approach.

This study extracted features from original EEG data and used a linear dynamic system approach to smooth these features and a manifold model was applied to find the trajectory of emotion changes.

The experiment results show that neural signatures associated with different emotions do exist and they share commonality across sessions and individuals, and the performance of deep models with shallow models is compared.

The proposed classification model is formed according to the current neuroscience trends, since it adopts the independency of two emotional dimensions, namely arousal and valence, as dictated by the bidirectional emotion theory.

This study applied machine-learning algorithms to categorize EEG dynamics according to subject self-reported emotional states during music listening to identify 30 subject-independent features that were most relevant to emotional processing across subjects and explored the feasibility of using fewer electrodes to characterize the EEG dynamics duringMusic listening.

A real-time fractal dimension (FD) based valence level recognition algorithm from Electroencephalographic (EEG) signals that is applied for recognition of 16 emotions defined by high/low arousal, high/ low dominance and 4 levels of valence dimension.

This work brings to the table the ECG signal and presents a thorough analysis of its psychological properties, differentiates for the first time between active and passive arousal, and advocates that there are higher chances of ECG reactivity to emotion when the induction method is active for the subject.

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