[HTML][HTML] Employing PCA and t-statistical approach for feature extraction and classification of emotion from multichannel EEG signal
To achieve a highly efficient brain-computer interface (BCI) system regarding emotion
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature …
recognition from electroencephalogram (EEG) signal, the most crucial issues are feature …
GMSS: Graph-based multi-task self-supervised learning for EEG emotion recognition
Previous electroencephalogram (EEG) emotion recognition relies on single-task learning,
which may lead to overfitting and learned emotion features lacking generalization. In this …
which may lead to overfitting and learned emotion features lacking generalization. In this …
Investigating EEG-based functional connectivity patterns for multimodal emotion recognition
Objective. Previous studies on emotion recognition from electroencephalography (EEG)
mainly rely on single-channel-based feature extraction methods, which ignore the functional …
mainly rely on single-channel-based feature extraction methods, which ignore the functional …
EEG-based emotion recognition using spatial-temporal graph convolutional LSTM with attention mechanism
L Feng, C Cheng, M Zhao, H Deng… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
The dynamic uncertain relationship among each brain region is a necessary factor that limits
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …
EEG-based emotion recognition. It is a thought-provoking problem to availably employ time …
Adaptive neural decision tree for EEG based emotion recognition
Y Zheng, J Ding, F Liu, D Wang - Information Sciences, 2023 - Elsevier
An adaptive neural decision tree is investigated to recognize electroencephalogram (EEG)
emotion signal with ability of intelligently selecting network structure. Firstly, to overcome …
emotion signal with ability of intelligently selecting network structure. Firstly, to overcome …
Phase-locking value based graph convolutional neural networks for emotion recognition
Z Wang, Y Tong, X Heng - Ieee Access, 2019 - ieeexplore.ieee.org
Recognition of discriminative neural signatures and regions corresponding to emotions are
important in understanding the neuron functional network underlying the human emotion …
important in understanding the neuron functional network underlying the human emotion …
Continuous convolutional neural network with 3D input for EEG-based emotion recognition
Y Yang, Q Wu, Y Fu, X Chen - … 2018, Siem Reap, Cambodia, December 13 …, 2018 - Springer
Automatic emotion recognition based on EEG is an important issue in Brain-Computer
Interface (BCI) applications. In this paper, baseline signals were taken into account to …
Interface (BCI) applications. In this paper, baseline signals were taken into account to …
Plug-and-play domain adaptation for cross-subject EEG-based emotion recognition
Human emotion decoding in affective brain-computer interfaces suffers a major setback due
to the inter-subject variability of electroencephalography (EEG) signals. Existing approaches …
to the inter-subject variability of electroencephalography (EEG) signals. Existing approaches …
Cross-subject EEG emotion recognition combined with connectivity features and meta-transfer learning
J Li, H Hua, Z Xu, L Shu, X Xu, F Kuang… - Computers in biology and …, 2022 - Elsevier
In recent years, with the rapid development of machine learning, automatic emotion
recognition based on electroencephalogram (EEG) signals has received increasing …
recognition based on electroencephalogram (EEG) signals has received increasing …
A channel-fused dense convolutional network for EEG-based emotion recognition
Human emotion recognition could greatly contribute to human–computer interaction with
promising applications in artificial intelligence. One of the challenges in recognition tasks is …
promising applications in artificial intelligence. One of the challenges in recognition tasks is …