Graph neural networks in EEG-based emotion recognition: a survey
Compared to other modalities, EEG-based emotion recognition can intuitively respond to the
emotional patterns in the human brain and, therefore, has become one of the most …
emotional patterns in the human brain and, therefore, has become one of the most …
A multi-dimensional graph convolution network for EEG emotion recognition
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 …
TFCNN-BiGRU with self-attention mechanism for automatic human emotion recognition using multi-channel EEG data
Electroencephalograms (EEG)-based technology for recognizing emotions has attracted a
lot of interest lately. However, there is still work to be done on the efficient fusion of different …
lot of interest lately. However, there is still work to be done on the efficient fusion of different …
Interpretable and robust ai in eeg systems: A survey
The close coupling of artificial intelligence (AI) and electroencephalography (EEG) has
substantially advanced human-computer interaction (HCI) technologies in the AI era …
substantially advanced human-computer interaction (HCI) technologies in the AI era …
A Comprehensive Survey on EEG-Based Emotion Recognition: A Graph-Based Perspective
Compared to other modalities, electroencephalogram (EEG) based emotion recognition can
intuitively respond to emotional patterns in the human brain and, therefore, has become one …
intuitively respond to emotional patterns in the human brain and, therefore, has become one …
Convolutional gated recurrent unit-driven multidimensional dynamic graph neural network for subject-independent emotion recognition
W Guo, Y Wang - Expert Systems with Applications, 2024 - Elsevier
Electroencephalogram (EEG) could directly reflect human brain activities. Recently, EEG-
based emotion recognition technology has attracted widespread attention. However …
based emotion recognition technology has attracted widespread attention. However …
Semi-supervised dual-stream self-attentive adversarial graph contrastive learning for cross-subject eeg-based emotion recognition
Electroencephalography (EEG) is an objective tool for emotion recognition with promising
applications. However, the scarcity of labeled data remains a major challenge in this field …
applications. However, the scarcity of labeled data remains a major challenge in this field …
EEGMatch: Learning With Incomplete Labels for Semisupervised EEG-Based Cross-Subject Emotion Recognition
Electroencephalography (EEG) is an objective tool for emotion recognition and shows
promising performance. However, the label scarcity problem is a main challenge in this field …
promising performance. However, the label scarcity problem is a main challenge in this field …
MAS-DGAT-Net: A dynamic graph attention network with multibranch feature extraction and staged fusion for EEG emotion recognition
In recent years, with the rise of deep learning technologies, EEG-based emotion recognition
has garnered significant attention. However, most existing methods tend to focus on the …
has garnered significant attention. However, most existing methods tend to focus on the …
Graph neural network-based eeg classification: A survey
Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …
emotion recognition, motor imagery and neurological diseases and disorders. A wide range …