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 …
Disentangled representation learning for multimodal emotion recognition
Multimodal emotion recognition aims to identify human emotions from text, audio, and visual
modalities. Previous methods either explore correlations between different modalities or …
modalities. Previous methods either explore correlations between different modalities or …
A review of Deep Learning based methods for Affect Analysis using Physiological Signals
Emotions are distinct reactions to internal or external events with implications for the
organism. Automatic emotion recognition is a demanding task for pattern recognition and a …
organism. Automatic emotion recognition is a demanding task for pattern recognition and a …
Physiological-signal-based emotion recognition: An odyssey from methodology to philosophy
W Li, Z Zhang, A Song - Measurement, 2021 - Elsevier
Exploration on emotions continues from past to present. Nowadays, with the rapid
advancement of intelligent technology, computer-aided emotion recognition using …
advancement of intelligent technology, computer-aided emotion recognition using …
Emotion recognition using heterogeneous convolutional neural networks combined with multimodal factorized bilinear pooling
Y Zhang, C Cheng, S Wang, T Xia - Biomedical Signal Processing and …, 2022 - Elsevier
Multimodal emotion recognition is one of the challenging topics in the field of knowledge-
based systems and many methods have been studied successfully. Nevertheless …
based systems and many methods have been studied successfully. Nevertheless …
Multimodal emotion recognition using a hierarchical fusion convolutional neural network
Y Zhang, C Cheng, Y Zhang - IEEE access, 2021 - ieeexplore.ieee.org
In recent years, deep learning has been increasingly used in the field of multimodal emotion
recognition in conjunction with electroencephalogram. Considering the complexity of …
recognition in conjunction with electroencephalogram. Considering the complexity of …
[HTML][HTML] Multi-feature input deep forest for EEG-based emotion recognition
Due to the rapid development of human–computer interaction, affective computing has
attracted more and more attention in recent years. In emotion recognition …
attracted more and more attention in recent years. In emotion recognition …
Multimodal emotion recognition based on manifold learning and convolution neural network
Y Zhang, C Cheng, YD Zhang - Multimedia Tools and Applications, 2022 - Springer
Multimodal emotion recognition task based on physiological signals is becoming a research
hotspot. Traditional methods need to design and extract a series of features from single …
hotspot. Traditional methods need to design and extract a series of features from single …
Personalized Deep Bi-LSTM RNN based model for pain intensity classification using EDA signal
Automatic pain intensity assessment from physiological signals has become an appealing
approach, but it remains a largely unexplored research topic. Most studies have used …
approach, but it remains a largely unexplored research topic. Most studies have used …
SFE-Net: EEG-based emotion recognition with symmetrical spatial feature extraction
X Deng, J Zhu, S Yang - Proceedings of the 29th ACM international …, 2021 - dl.acm.org
Emotion recognition based on EEG (electroencephalography) has been widely used in
human-computer interaction, distance education and health care. However, the …
human-computer interaction, distance education and health care. However, the …