Review of studies on emotion recognition and judgment based on physiological signals
W Lin, C Li - Applied Sciences, 2023 - mdpi.com
People's emotions play an important part in our daily life and can not only reflect
psychological and physical states, but also play a vital role in people's communication …
psychological and physical states, but also play a vital role in people's communication …
A systematic literature review of emotion recognition using EEG signals
In this study, we conducted a systematic literature review of 107 primary studies conducted
between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to …
between 2017 and 2023 to discern trends in datasets, classifiers, and contributions to …
Brain emotion perception inspired EEG emotion recognition with deep reinforcement learning
Inspired by the well-known Papez circuit theory and neuroscience knowledge of
reinforcement learning, a double dueling deep network (DQN) is built incorporating the …
reinforcement learning, a double dueling deep network (DQN) is built incorporating the …
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 …
Temporal relative transformer encoding cooperating with channel attention for EEG emotion analysis
Electroencephalogram (EEG)-based emotion computing has become a hot topic of brain-
computer fusion. EEG signals have inherent temporal and spatial characteristics. However …
computer fusion. EEG signals have inherent temporal and spatial characteristics. However …
A Bi-Stream hybrid model with MLPBlocks and self-attention mechanism for EEG-based emotion recognition
Due to the instability and complex distribution of electroencephalography (EEG) signals and
the great cross-subject variations, exploiting valuable and discriminative emotional …
the great cross-subject variations, exploiting valuable and discriminative emotional …
Research Progress of EEG-Based Emotion Recognition: A Survey
Emotion recognition based on electroencephalography (EEG) signals has emerged as a
prominent research field, facilitating objective evaluation of diseases like depression and …
prominent research field, facilitating objective evaluation of diseases like depression and …
Global adaptive transformer for cross-subject enhanced EEG classification
Due to the individual difference, EEG signals from other subjects (source) can hardly be
used to decode the mental intentions of the target subject. Although transfer learning …
used to decode the mental intentions of the target subject. Although transfer learning …
STSNet: a novel spatio-temporal-spectral network for subject-independent EEG-based emotion recognition
R Li, C Ren, S Zhang, Y Yang, Q Zhao, K Hou… - … Information Science and …, 2023 - Springer
How to use the characteristics of EEG signals to obtain more complementary and
discriminative data representation is an issue in EEG-based emotion recognition. Many …
discriminative data representation is an issue in EEG-based emotion recognition. Many …
Progressive graph convolution network for EEG emotion recognition
Studies in the area of neuroscience have revealed the relationship between emotional
patterns and brain functional regions, demonstrating that the dynamic relationship between …
patterns and brain functional regions, demonstrating that the dynamic relationship between …