Emotion recognition in EEG signals using deep learning methods: A review

M Jafari, A Shoeibi, M Khodatars… - Computers in Biology …, 2023 - Elsevier
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

Generative technology for human emotion recognition: A scoping review

F Ma, Y Yuan, Y Xie, H Ren, I Liu, Y He, F Ren, FR Yu… - Information …, 2024 - Elsevier
Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue
machines with the ability to comprehend and respond to human emotions. Central to this …

DA-CapsNet: A multi-branch capsule network based on adversarial domain adaption for cross-subject EEG emotion recognition

S Liu, Z Wang, Y An, B Li, X Wang, Y Zhang - Knowledge-Based Systems, 2024 - Elsevier
Due to inter-individual variances, cross-subject electroencephalogram (EEG)-based
emotion recognition is a challenging task. In this paper, we construct a multi-branch Capsule …

Self-supervised group meiosis contrastive learning for eeg-based emotion recognition

H Kan, J Yu, J Huang, Z Liu, H Wang, H Zhou - Applied Intelligence, 2023 - Springer
The progress of EEG-based emotion recognition has received widespread attention from the
fields of human-machine interaction and cognitive science. However, recognizing emotions …

[HTML][HTML] Lightweight attention mechanisms for EEG emotion recognition for brain computer interface

NK Gunda, MI Khalaf, S Bhatnagar, A Quraishi… - Journal of Neuroscience …, 2024 - Elsevier
Background In the realm of brain-computer interfaces (BCI), identifying emotions from
electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the …

An attention-based multi-domain bi-hemisphere discrepancy feature fusion model for EEG emotion recognition

L Gong, W Chen, D Zhang - IEEE Journal of Biomedical and …, 2024 - ieeexplore.ieee.org
Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in
the field of brain-computer interface. Previous emotion recognition methods have …

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 …

MAS-DGAT-Net: A dynamic graph attention network with multibranch feature extraction and staged fusion for EEG emotion recognition

S Liu, X Wang, M Jiang, Y An, Z Gu, B Li… - Knowledge-Based …, 2024 - Elsevier
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 …

Estimating Addiction-Related Brain Connectivity by Prior-Embedding Graph Generative Adversarial Networks

C Jing, Y Shen, S Zhao, Y Pan… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
The study of nicotine addiction mechanism is of great significance in both nicotine
withdrawal and brain science. The detection of addiction-related brain connectivity using …

[HTML][HTML] Domain adversarial learning with multiple adversarial tasks for EEG emotion recognition

X Ju, X Wu, S Dai, M Li, D Hu - Expert Systems with Applications, 2025 - Elsevier
Abstract Domain adaptation methods using electroencephalography (EEG) play an
important role in cross-subject emotion recognition. However, enhancing the generalizability …