A review of Graph Neural Networks for Electroencephalography data analysis

Graña, Manuel; Morais-Quilez, Igone

doi:10.1016/j.neucom.2023.126901

Cited by 5 publications

(1 citation statement)

References 84 publications

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“…In recent years, graph neural networks (GNNs) have emerged as a prominent tool in deep learning, driving significant advancements across various domains. In the realm of EEG-based emotion recognition, GNNs offer a unique advantage by simultaneously considering the local neighborhoods of nodes and the global structure of the entire graph [15][16][17][18]. This enables better capture of both local and global information embedded in EEG data, thereby enhancing the model's understanding and representation of EEG data and providing a promising avenue for analyzing complex brain networks and extracting nuanced emotional features [19][20][21][22].…”

Section: Introductionmentioning

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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