A Multi-Dimensional Graph Convolution Network for EEG Emotion Recognition

Du, Guanglong; Su, Jinshao; Zhang, Linlin; Su, Kang; Wang, Xueqian; Teng, Shaohua; Liu, Peter

doi:10.1109/tim.2022.3204314

Cited by 30 publications

(17 citation statements)

References 40 publications

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“…TJU dataset: Experiments were conducted with 25 righthanded students (12 men and 13 women) at Tianjin University, their average age is 25.3 years (range, [19][20][21][22][23][24][25][26][27][28][29][30][31][32]. None of them have personal or family history of neurological illness.…”

Section: A Experimental Protocol and Data Preprocessingmentioning

confidence: 99%

“…Li et al [20] used mutual information to construct the adjacency matrix. Du et al [21] used spatial distance matrix and relational communication matrix to initialize the adjacency matrix. However, most of the existing work has focused on the design of adjacency matrices to improve the decoding accuracy, which often requires manual design or requires a priori knowledge.…”

Section: Introductionmentioning

confidence: 99%

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Graph Convolution Neural Network Based End-to-End Channel Selection and Classification for Motor Imagery Brain–Computer Interfaces

Sun

Liu

et al. 2023

IEEE Trans. Ind. Inf.

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Classification of electroencephalogram-based motor imagery (MI-EEG) tasks is crucial in brain-computer interface (BCI). EEG signals require a large number of channels in the acquisition process, which hinders its application in practice. How to select the optimal channel subset without a serious impact on the classification performance is an urgent problem to be solved in the field of BCIs. This article proposes an end-to-end deep learning framework, called EEG channel active inference neural network (EEG-ARNN), which is based on graph convolutional neural networks (GCN) to fully exploit the correlation of signals in the temporal and spatial domains. Two channel selection methods, i.e., edge-selection (ES) and aggregationselection (AS), are proposed to select a specified number of optimal channels automatically. Two publicly available BCI Competition IV 2a (BCICIV 2a) dataset and PhysioNet dataset and a self-collected dataset (TJU dataset) are used to evaluate the performance of the proposed method. Experimental results reveal that the proposed method outperforms state-of-the-art methods in terms of both classification accuracy and robustness. Using only a small number of channels, we obtain a classification performance similar to that of using all channels. Finally, the association between selected channels and activated brain areas is analyzed,

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Section: A Experimental Protocol and Data Preprocessingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Graph Convolution Neural Network Based End-to-End Channel Selection and Classification for Motor Imagery Brain–Computer Interfaces

Sun

Liu

et al. 2023

IEEE Trans. Ind. Inf.

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“…These methods can be generally categorised as learnable or pre-defined. Multiple/Combined graph definitions -- [47], [49], [53], [54], [57]- [59], [61]- [64], [67], [69], [72], [79], [79], [81], [82], [87], [92], [102] [51], [53], [55], [57], [71], [72], [75], [78], [81], [82], [87], [89], [90], [92], [93], [93], [95]- [99], [101], [102] Raw signal ✓ ✗ ✗…”

Section: Definition Of Brain Graph Structurementioning

confidence: 99%

“…Thus, it might not accurately represent the underlying brain network. Some papers propose to overcome this limitation by manually inserting global [53], [56]- [58], [62] or inter-hemispheric edges [46], [54], [87].…”

Section: Definition Of Brain Graph Structurementioning

confidence: 99%

Graph Neural Network-Based EEG Classification: A Survey

Klepl,

Wu,

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disorders. A wide range of methods have been proposed to design GNN-based classifiers. Therefore, there is a need for a systematic review and categorisation of these approaches. We exhaustively search the published literature on this topic and derive several categories for comparison. These categories highlight the similarities and differences among the methods. The results suggest a prevalence of spectral graph convolutional layers over spatial. Additionally, we identify standard forms of node features, with the most popular being the raw EEG signal and differential entropy. Our results summarise the emerging trends in GNN-based approaches for EEG classification. Finally, we discuss several promising research directions, such as exploring the potential of transfer learning methods and appropriate modelling of cross-frequency interactions.

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“…Based on this type of data set, most existing investigations with a combination of EEG and peripheral physiological activity measures have used overall (summative) retrospective ratings of emotional valence and arousal for each trial as outcome variables for modeling and often used a classification approach, that is, high versus low arousal (Shon et al, 2018) or emotion categories (Du et al, 2022; Thejaswini & Ravikumar, 2020). These attempts have used different classification algorithms such as K‐nearest neighbors (KNN), convolutional neural networks (CNN), etc., along with various methods for selecting features, and have been able to predict high versus low reported arousal and valence with a high degree of accuracy (Ayata et al, 2017; Chen et al, 2019; Koelstra et al, 2012; Shon et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Emotional arousal pattern (EMAP): A new database for modeling momentary subjective and psychophysiological responding to affective stimuli

Eisenbarth,

Oxner,

Shehu

et al. 2023

Psychophysiology

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This article describes a new database (named “EMAP”) of 145 individuals' reactions to emotion‐provoking film clips. It includes electroencephalographic and peripheral physiological data as well as moment‐by‐moment ratings for emotional arousal in addition to overall and categorical ratings. The resulting variation in continuous ratings reflects inter‐individual variability in emotional responding. To make use of the moment‐by‐moment data for ratings as well as neurophysiological activity, we used a machine learning approach. The results show that algorithms that are based on temporal information improve predictions compared to algorithms without a temporal component, both within and across participant modeling. Although predicting moment‐by‐moment changes in emotional experiences by analyzing neurophysiological activity was more difficult than using aggregated experience ratings, selecting a subset of predictors improved the prediction. This also showed that not only single features, for example, skin conductance, but a range of neurophysiological parameters explain variation in subjective fluctuations of subjective experience.

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A Multi-Dimensional Graph Convolution Network for EEG Emotion Recognition

Cited by 30 publications

References 40 publications

Graph Convolution Neural Network Based End-to-End Channel Selection and Classification for Motor Imagery Brain–Computer Interfaces

Graph Convolution Neural Network Based End-to-End Channel Selection and Classification for Motor Imagery Brain–Computer Interfaces

Graph Neural Network-Based EEG Classification: A Survey

Emotional arousal pattern (EMAP): A new database for modeling momentary subjective and psychophysiological responding to affective stimuli

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