Research Progress of EEG-Based Emotion Recognition: A Survey

Y Wang, B Zhang, L Di - ACM Computing Surveys, 2024 - dl.acm.org
Emotion recognition based on electroencephalography (EEG) signals has emerged as a
prominent research field, facilitating objective evaluation of diseases like depression and …

An auto-weighting incremental random vector functional link network for EEG-based driving fatigue detection

Y Zhang, R Guo, Y Peng, W Kong… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recently, electroencephalogram (EEG) has been receiving increasing attention in driving
fatigue attention because it is generated by the neural activities of central nervous system …

S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram

A Abgeena, S Garg - Health Information Science and Systems, 2023 - Springer
Purpose Human emotion recognition using electroencephalograms (EEG) is a critical area
of research in human–machine interfaces. Furthermore, EEG data are convoluted and …

Dynamical graph neural network with attention mechanism for epilepsy detection using single channel EEG

Y Li, Y Yang, Q Zheng, Y Liu, H Wang, S Song… - Medical & Biological …, 2024 - Springer
Epilepsy is a chronic brain disease, and identifying seizures based on
electroencephalogram (EEG) signals would be conducive to implement interventions to help …

Emotion recognition from EEG signals of hearing-impaired people using stacking ensemble learning framework based on a novel brain network

Q Kang, Q Gao, Y Song, Z Tian, Y Yang… - IEEE Sensors …, 2021 - ieeexplore.ieee.org
Emotion recognition based on electroencephalography (EEG) signals has become an
interesting research topic in the field of neuroscience, psychology, neural engineering, and …

A multi-source transfer joint matching method for inter-subject motor imagery decoding

F Wei, X Xu, T Jia, D Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Individual differences among different subjects pose a great challenge to motor imagery (MI)
decoding. Multi-source transfer learning (MSTL) is one of the most promising ways to reduce …

[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 …

Q-learning guided mutational Harris hawk optimizer for high-dimensional gene data feature selection

L Peng, X Li, L Yu, AA Heidari, H Chen, G Liang - Applied Soft Computing, 2024 - Elsevier
With the widespread application of high-throughput sequencing technology in recent years,
the scale of high-dimensional gene sequence datasets has rapidly expanded. However, due …

A standardized database of Chinese emotional short videos based on age and gender differences

D Duan, W Zhong, S Ran, L Ye, Q Zhang - PloS One, 2023 - journals.plos.org
Most of the existing emotion elicitation databases use the film clips as stimuli and do not take
into account the age and gender differences of participants. Considering the short videos …

Embedded EEG Feature Selection for Multi-Dimension Emotion Recognition via Local and Global Label Relevance

X Xu, F Wei, T Jia, L Zhuo, H Zhang… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Due to the problem of a small amount of EEG samples and relatively high dimensionality of
electroencephalogram (EEG) features, feature selection plays an essential role in EEG …