EEG emotion recognition using dynamical graph convolutional neural networks
In this paper, a multichannel EEG emotion recognition method based on a novel dynamical
graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed …
graph convolutional neural networks (DGCNN) is proposed. The basic idea of the proposed …
EEG emotion recognition using fusion model of graph convolutional neural networks and LSTM
In recent years, graph convolutional neural networks have become research focus and
inspired new ideas for emotion recognition based on EEG. Deep learning has been widely …
inspired new ideas for emotion recognition based on EEG. Deep learning has been widely …
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 …
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 …
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 …
Phase-locking value based graph convolutional neural networks for emotion recognition
Z Wang, Y Tong, X Heng - Ieee Access, 2019 - ieeexplore.ieee.org
Recognition of discriminative neural signatures and regions corresponding to emotions are
important in understanding the neuron functional network underlying the human emotion …
important in understanding the neuron functional network underlying the human emotion …
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 …
EEG emotion recognition using improved graph neural network with channel selection
X Lin, J Chen, W Ma, W Tang, Y Wang - Computer Methods and Programs …, 2023 - Elsevier
Background and objective: Emotion classification tasks based on electroencephalography
(EEG) are an essential part of artificial intelligence, with promising applications in healthcare …
(EEG) are an essential part of artificial intelligence, with promising applications in healthcare …
Emotion recognition using spatial-temporal EEG features through convolutional graph attention network
Objective. Constructing an efficient human emotion recognition model based on
electroencephalogram (EEG) signals is significant for realizing emotional brain–computer …
electroencephalogram (EEG) signals is significant for realizing emotional brain–computer …
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 …