Emotion recognition in EEG signals using deep learning methods: A review
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 …
planning, reasoning, and other mental states. As a result, they are considered a significant …
[HTML][HTML] Integrated deep learning paradigm for document-based sentiment analysis
P Atandoh, F Zhang, D Adu-Gyamfi, PH Atandoh… - Journal of King Saud …, 2023 - Elsevier
An integrated deep learning paradigm for the analysis of document-based sentiments is
presented in this article. Generally, sentiment analysis has enormous applications in the real …
presented in this article. Generally, sentiment analysis has enormous applications in the real …
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 …
[HTML][HTML] Capsule Network with Its Limitation, Modification, and Applications—A Survey
Numerous advancements in various fields, including pattern recognition and image
classification, have been made thanks to modern computer vision and machine learning …
classification, have been made thanks to modern computer vision and machine learning …
Light-weight residual convolution-based capsule network for EEG emotion recognition
In recent years, electroencephalography (EEG) emotion recognition has achieved excellent
progress. However, the applied shallow convolutional neural networks (CNNs) cannot …
progress. However, the applied shallow convolutional neural networks (CNNs) cannot …
A multi-head residual connection GCN for EEG emotion recognition
X Qiu, S Wang, R Wang, Y Zhang, L Huang - Computers in Biology and …, 2023 - Elsevier
Electroencephalography (EEG) emotion recognition is a crucial aspect of human-computer
interaction. However, conventional neural networks have limitations in extracting profound …
interaction. However, conventional neural networks have limitations in extracting profound …
ICaps-ResLSTM: Improved capsule network and residual LSTM for EEG emotion recognition
Electroencephalography (EEG) emotion recognition is an important task for brain–computer
interfaces. The time, frequency, and spatial domains of EEG signals have been widely …
interfaces. The time, frequency, and spatial domains of EEG signals have been widely …
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 …
Cross-subject EEG linear domain adaption based on batch normalization and depthwise convolutional neural network
Electroencephalogram (EEG)-based emotion recognition has been widely used in affective
computing. However, the study on improving recognition accuracy across individuals is …
computing. However, the study on improving recognition accuracy across individuals is …
A bidirectional interaction-based hybrid network architecture for eeg cognitive recognition
Background and objective: Extracting cognitive representation and computational
representation information simultaneously from electroencephalography (EEG) data and …
representation information simultaneously from electroencephalography (EEG) data and …