EEG-based emotion recognition using regularized graph neural networks
Electroencephalography (EEG) measures the neuronal activities in different brain regions
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
via electrodes. Many existing studies on EEG-based emotion recognition do not fully exploit …
A multi-domain adaptive graph convolutional network for EEG-based emotion recognition
Among all solutions of emotion recognition tasks, electroencephalogram (EEG) is a very
effective tool and has received broad attention from researchers. In addition, information …
effective tool and has received broad attention from researchers. In addition, information …
A multi-dimensional graph convolution network for EEG emotion recognition
G Du, J Su, L Zhang, K Su, X Wang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Due to the changeable, high-dimensional, nonstationary, and other characteristics of
electroencephalography (EEG) signals, the recognition of EEG signals is mostly limited to …
electroencephalography (EEG) signals, the recognition of EEG signals is mostly limited to …
Hierarchical dynamic graph convolutional network with interpretability for EEG-based emotion recognition
Graph convolutional networks (GCNs) have shown great prowess in learning topological
relationships among electroencephalogram (EEG) channels for EEG-based emotion …
relationships among electroencephalogram (EEG) channels for EEG-based emotion …
Cross-subject EEG emotion recognition with self-organized graph neural network
As a physiological process and high-level cognitive behavior, emotion is an important
subarea in neuroscience research. Emotion recognition across subjects based on brain …
subarea in neuroscience research. Emotion recognition across subjects based on brain …
EEG-GCN: spatio-temporal and self-adaptive graph convolutional networks for single and multi-view EEG-based emotion recognition
Y Gao, X Fu, T Ouyang, Y Wang - IEEE Signal Processing …, 2022 - ieeexplore.ieee.org
Graph networks are naturally suitable for modeling multi-channel features of EEG signals.
However, the existing study that attempts to utilize graph-based neural networks for EEG …
However, the existing study that attempts to utilize graph-based neural networks for EEG …
SparseDGCNN: Recognizing emotion from multichannel EEG signals
Emotion recognition from EEG signals has attracted much attention in affective computing.
Recently, a novel dynamic graph convolutional neural network (DGCNN) model was …
Recently, a novel dynamic graph convolutional neural network (DGCNN) model was …
STGATE: Spatial-temporal graph attention network with a transformer encoder for EEG-based emotion recognition
Electroencephalogram (EEG) is a crucial and widely utilized technique in neuroscience
research. In this paper, we introduce a novel graph neural network called the spatial …
research. In this paper, we introduce a novel graph neural network called the spatial …
Graph-embedded convolutional neural network for image-based EEG emotion recognition
Emotion recognition from electroencephalograph (EEG) signals has long been essential for
affective computing. In this article, we evaluate EEG emotion recognition by converting EEG …
affective computing. In this article, we evaluate EEG emotion recognition by converting EEG …
A domain generative graph network for EEG-based emotion recognition
Emotion is a human attitude experience and corresponding behavioral response to
objective things. Effective emotion recognition is important for the intelligence and …
objective things. Effective emotion recognition is important for the intelligence and …