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 …
PR-PL: A novel prototypical representation based pairwise learning framework for emotion recognition using EEG signals
Affective brain-computer interface based on electroencephalography (EEG) is an important
branch in the field of affective computing. However, the individual differences in EEG …
branch in the field of affective computing. However, the individual differences in EEG …
PR-PL: A novel transfer learning framework with prototypical representation based pairwise learning for EEG-based emotion recognition
Affective brain-computer interfaces based on electroencephalography (EEG) is an important
branch in the field of affective computing. However, individual differences and noisy labels …
branch in the field of affective computing. However, individual differences and noisy labels …
Multi-view domain-adaptive representation learning for EEG-based emotion recognition
Current research suggests that there exist certain limitations in EEG emotion recognition,
including redundant and meaningless time-frames and channels, as well as inter-and intra …
including redundant and meaningless time-frames and channels, as well as inter-and intra …
Learning a robust unified domain adaptation framework for cross-subject EEG-based emotion recognition
M Jiménez-Guarneros, G Fuentes-Pineda - Biomedical Signal Processing …, 2023 - Elsevier
Over the last few years, unsupervised domain adaptation (UDA) based on deep learning
has emerged as a solution to build cross-subject emotion recognition models from …
has emerged as a solution to build cross-subject emotion recognition models from …
[HTML][HTML] Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods
A systematic review on machine-learning strategies for improving generalization in
electroencephalography-based emotion classification was realized. In particular, cross …
electroencephalography-based emotion classification was realized. In particular, cross …
Gusa: Graph-based unsupervised subdomain adaptation for cross-subject eeg emotion recognition
EEG emotion recognition has been hampered by the clear individual differences in the
electroencephalogram (EEG). Nowadays, domain adaptation is a good way to deal with this …
electroencephalogram (EEG). Nowadays, domain adaptation is a good way to deal with this …
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 …
FMLAN: A novel framework for cross-subject and cross-session EEG emotion recognition
P Yu, X He, H Li, H Dou, Y Tan, H Wu… - … Signal Processing and …, 2025 - Elsevier
Emotion recognition is significant in brain-computer interface (BCI) applications.
Electroencephalography (EEG) is extensively employed for emotion recognition because of …
Electroencephalography (EEG) is extensively employed for emotion recognition because of …
Benchmarking domain generalization on EEG-based emotion recognition
Electroencephalography (EEG) based emotion recognition has demonstrated tremendous
improvement in recent years. Specifically, numerous domain adaptation (DA) algorithms …
improvement in recent years. Specifically, numerous domain adaptation (DA) algorithms …