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 …
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 …
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 …
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 …
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 …
[HTML][HTML] 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 …
[HTML][HTML] EEG-based emotion recognition using hybrid CNN and LSTM classification
B Chakravarthi, SC Ng, MR Ezilarasan… - Frontiers in …, 2022 - frontiersin.org
Emotions are a mental state that is accompanied by a distinct physiologic rhythm, as well as
physical, behavioral, and mental changes. In the latest days, physiological activity has been …
physical, behavioral, and mental changes. In the latest days, physiological activity has been …
EEG-based emotion recognition with feature fusion networks
Q Gao, Y Yang, Q Kang, Z Tian, Y Song - International journal of machine …, 2022 - Springer
With the rapid development of Human-computer interaction, automatic emotion recognition
based on multichannel electroencephalography (EEG) signals has attracted much attention …
based on multichannel electroencephalography (EEG) signals has attracted much attention …
Recognition of Emotions Using Multichannel EEG Data and DBN‐GC‐Based Ensemble Deep Learning Framework
H Chao, H Zhi, L Dong, Y Liu - Computational intelligence and …, 2018 - Wiley Online Library
Fusing multichannel neurophysiological signals to recognize human emotion states
becomes increasingly attractive. The conventional methods ignore the complementarity …
becomes increasingly attractive. The conventional methods ignore the complementarity …