EEG-based emotion recognition using graph convolutional neural network with dual attention mechanism
W Chen, Y Liao, R Dai, Y Dong… - Frontiers in Computational …, 2024 - frontiersin.org
EEG-based emotion recognition is becoming crucial in brain-computer interfaces (BCI).
Currently, most researches focus on improving accuracy, while neglecting further research …
Currently, most researches focus on improving accuracy, while neglecting further research …
Spatial-Temporal Mamba Network for EEG-based Motor Imagery Classification
X Yang, Z Jia - International Conference on Advanced Data Mining …, 2024 - Springer
Motor imagery (MI) classification is key for brain-computer interfaces (BCIs). Until recent
years, numerous models had been proposed, ranging from classical algorithms like …
years, numerous models had been proposed, ranging from classical algorithms like …
Vision Paper: Designing Graph Neural Networks in Compliance with the European Artificial Intelligence Act
The European Union's Artificial Intelligence Act (AI Act) introduces comprehensive
guidelines for the development and oversight of Artificial Intelligence (AI) and Machine …
guidelines for the development and oversight of Artificial Intelligence (AI) and Machine …
Self supervised learning based emotion recognition using physiological signals
M Zhang, YL Cui - Frontiers in Human Neuroscience, 2024 - frontiersin.org
Introduction The significant role of emotional recognition in the field of human-machine
interaction has garnered the attention of many researchers. Emotion recognition based on …
interaction has garnered the attention of many researchers. Emotion recognition based on …
TSANN-TG: Temporal–Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition
C Jiang, Y Dai, Y Ding, X Chen, Y Li, Y Tang - Brain Sciences, 2024 - mdpi.com
Electroencephalography (EEG)-based emotion recognition is increasingly pivotal in the
realm of affective brain–computer interfaces. In this paper, we propose TSANN-TG (temporal …
realm of affective brain–computer interfaces. In this paper, we propose TSANN-TG (temporal …
A new fuzzy-based ensemble framework based on attention-based deep learning architectures for automated detection of abnormal EEG
Z Yang, S Li - International Journal of System Assurance Engineering …, 2024 - Springer
Biomedical science research encompasses a wide array of fields such as biomedical
engineering, gene analysis, biomedical signal and image processing. The significance of …
engineering, gene analysis, biomedical signal and image processing. The significance of …
[PDF][PDF] VSGT: variational spatial and gaussian temporal graph models for EEG-based emotion recognition
Electroencephalogram (EEG), which directly reflects the emotional activity of the brain, has
been increasingly utilized for emotion recognition. Most works exploit the spatial and …
been increasingly utilized for emotion recognition. Most works exploit the spatial and …
A Multimodal Knowledge Distillation Framework for Sleep Physiological Data
Sleep staging helps to make an accurate diagnosis of sleep disorders. Analysis of
physiological sleep data helps to identify specific sleep stages. In particular, the use of …
physiological sleep data helps to identify specific sleep stages. In particular, the use of …
Advancing EEG-Based Emotion Recognition: Multimodal Techniques, Channel Optimization, and Insights into Subjective Emotion Perception
SY Dharia - 2024 - winnspace.uwinnipeg.ca
This dissertation explores the application of electroencephalography (EEG) in identifying
and understanding the neural mechanisms of emotional responses. Using a non-invasive …
and understanding the neural mechanisms of emotional responses. Using a non-invasive …
VBH-GNN: Variational Bayesian Heterogeneous Graph Neural Networks for Cross-subject Emotion Recognition
The research on human emotion under electroencephalogram (EEG) is an emerging field in
which cross-subject emotion recognition (ER) is a promising but challenging task. Many …
which cross-subject emotion recognition (ER) is a promising but challenging task. Many …