In this paper, we propose a Multi-Domain Adaptive Graph Convolutional Network (MD-AGCN), fusing the knowledge of both the frequency domain and the temporal ...
ABSTRACT. Among all solutions of emotion recognition tasks, electroencephalo- gram (EEG) is a very effective tool and has received broad attention.
This study proposes a new end-to-end emotion recognition method based on brain connectivity features and domain adaptive residual convolutional network.
Thus, EEG-based emotion recognition has evolved rapidly in recent years with many excellent studies exploiting intact EEG data in a supervised manner [10, 14, ...
At the same time, the results show that MD-AGCN could extract complementary domain information and exploit channel relationships for EEG-based emotion ...
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Aug 30, 2024 · Graph Neural Networks (GNNs) emerge as a powerful tool for modeling dependencies of emotional EEG within the network neuroscience framework.
Apr 18, 2023 · This section reviews the related work from three aspects: EEG emotion recognition, graph convolutional neural network and hierarchical graph ...
This paper introduces a novel multi-head residual graph convolutional neural network (MRGCN) model that incorporates complex brain networks and graph ...
Missing: Adaptive | Show results with:Adaptive
Jul 30, 2024 · A novel architecture that effectively combines multi-view adjacency matrices with a hierarchical attention mechanism and domain adaptation for robust EEG-based ...
Jul 18, 2024 · Specifically, we utilize graph convolutional neural networks to model the brain network as a graph to extract representative spatial features.
Missing: Adaptive | Show results with:Adaptive