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] A systematic review on automated human emotion recognition using electroencephalogram signals and artificial intelligence
Abstract Brain-Computer Interaction (BCI) system intelligence has become more dependent
on electroencephalogram (EEG)-based emotion recognition because of the numerous …
on electroencephalogram (EEG)-based emotion recognition because of the numerous …
EEG-based cross-subject emotion recognition using Fourier-Bessel series expansion based empirical wavelet transform and NCA feature selection method
Automated emotion recognition using brain electroencephalogram (EEG) signals is
predominantly used for the accurate assessment of human actions as compared to facial …
predominantly used for the accurate assessment of human actions as compared to facial …
Subject-independent EEG emotion recognition with hybrid spatio-temporal GRU-Conv architecture
G Xu, W Guo, Y Wang - Medical & Biological Engineering & Computing, 2023 - Springer
Recently, various deep learning frameworks have shown excellent performance in decoding
electroencephalogram (EEG) signals, especially in human emotion recognition. However …
electroencephalogram (EEG) signals, especially in human emotion recognition. However …
Emotion recognition using spatial-temporal EEG features through convolutional graph attention network
Objective. Constructing an efficient human emotion recognition model based on
electroencephalogram (EEG) signals is significant for realizing emotional brain–computer …
electroencephalogram (EEG) signals is significant for realizing emotional brain–computer …
Deep time-frequency features and semi-supervised dimension reduction for subject-independent emotion recognition from multi-channel EEG signals
B Zali-Vargahan, A Charmin, H Kalbkhani… - … Signal Processing and …, 2023 - Elsevier
In recent years, human emotion recognition has received great attention since it plays an
essential role in human-computer interactions. Traditional methods focused on …
essential role in human-computer interactions. Traditional methods focused on …
Capsule neural networks on spatio-temporal EEG frames for cross-subject emotion recognition
Scalp EEG plots are plots of scalp potentials against time, and hence, capture spatial
information, owing to the placement of electrodes on the scalp, as well as, temporal …
information, owing to the placement of electrodes on the scalp, as well as, temporal …
Deep BiLSTM neural network model for emotion detection using cross-dataset approach
VM Joshi, RB Ghongade, AM Joshi… - … Signal Processing and …, 2022 - Elsevier
The purpose of this research is to use a cross-dataset approach to construct an EEG-based
emotion recognition system. So far, numerous modeling strategies for emotion recognition …
emotion recognition system. So far, numerous modeling strategies for emotion recognition …
EESCN: A novel spiking neural network method for EEG-based emotion recognition
FF Xu, D Pan, H Zheng, Y Ouyang, Z Jia… - Computer methods and …, 2024 - Elsevier
Abstract Background and Objective Although existing artificial neural networks have
achieved good results in electroencephalograph (EEG) emotion recognition, further …
achieved good results in electroencephalograph (EEG) emotion recognition, further …
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 …