Two-level domain adaptation neural network for EEG-based emotion recognition
G Bao, N Zhuang, L Tong, B Yan, J Shu… - Frontiers in Human …, 2021 - frontiersin.org
Emotion recognition plays an important part in human-computer interaction (HCI). Currently,
the main challenge in electroencephalogram (EEG)-based emotion recognition is the non …
the main challenge in electroencephalogram (EEG)-based emotion recognition is the non …
Learning CNN features from DE features for EEG-based emotion recognition
Recently, deep neural networks (DNNs) have shown the remarkable success of feature
representations in computer vision, audio analysis, and natural language processing …
representations in computer vision, audio analysis, and natural language processing …
EEG based emotion recognition using fusion feature extraction method
Q Gao, C Wang, Z Wang, X Song, E Dong… - Multimedia Tools and …, 2020 - Springer
As a high-level function of the human brain, emotion is the external manifestation of people's
psychological characteristics. The emotion has a great impact on people's personality and …
psychological characteristics. The emotion has a great impact on people's personality and …
MTLFuseNet: a novel emotion recognition model based on deep latent feature fusion of EEG signals and multi-task learning
R Li, C Ren, Y Ge, Q Zhao, Y Yang, Y Shi… - Knowledge-Based …, 2023 - Elsevier
How to extract discriminative latent feature representations from electroencephalography
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …
(EEG) signals and build a generalized model is a topic in EEG-based emotion recognition …
Internal emotion classification using EEG signal with sparse discriminative ensemble
Among various physiological signal acquisition methods for the study of the human brain,
EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive …
EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive …
On-road driver emotion recognition using facial expression
H Xiao, W Li, G Zeng, Y Wu, J Xue, J Zhang, C Li… - Applied Sciences, 2022 - mdpi.com
With the development of intelligent automotive human-machine systems, driver emotion
detection and recognition has become an emerging research topic. Facial expression-based …
detection and recognition has become an emerging research topic. Facial expression-based …
A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition
R Li, C Ren, X Zhang, B Hu - Computers in biology and medicine, 2022 - Elsevier
Emotion recognition is a vital but challenging step in creating passive brain-computer
interface applications. In recent years, many studies on electroencephalogram (EEG)-based …
interface applications. In recent years, many studies on electroencephalogram (EEG)-based …
Exploring self-attention graph pooling with EEG-based topological structure and soft label for depression detection
T Chen, Y Guo, S Hao, R Hong - IEEE transactions on affective …, 2022 - ieeexplore.ieee.org
Electroencephalogram (EEG) has been widely used in neurological disease detection, ie,
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …
major depressive disorder (MDD). Recently, some deep EEG-based MDD detection …
EEG-based emotion charting for Parkinson's disease patients using Convolutional Recurrent Neural Networks and cross dataset learning
Electroencephalogram (EEG) based emotion classification reflects the actual and intrinsic
emotional state, resulting in more reliable, natural, and meaningful human-computer …
emotional state, resulting in more reliable, natural, and meaningful human-computer …
Machine-learning-based emotion recognition system using EEG signals
R Alhalaseh, S Alasasfeh - Computers, 2020 - mdpi.com
Many scientific studies have been concerned with building an automatic system to recognize
emotions, and building such systems usually relies on brain signals. These studies have …
emotions, and building such systems usually relies on brain signals. These studies have …