Internal emotion classification using EEG signal with sparse discriminative ensemble
Among various physiological signal acquisition methods for the study of the human brain,
EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive …
EEG (Electroencephalography) is more effective. EEG provides a convenient, non-intrusive …
Multi-domain feature fusion for emotion classification using DEAP dataset
Emotion recognition in real-time using electroencephalography (EEG) signals play a key
role in human-computer interaction and affective computing. The existing emotion …
role in human-computer interaction and affective computing. The existing emotion …
SparseDGCNN: Recognizing emotion from multichannel EEG signals
Emotion recognition from EEG signals has attracted much attention in affective computing.
Recently, a novel dynamic graph convolutional neural network (DGCNN) model was …
Recently, a novel dynamic graph convolutional neural network (DGCNN) model was …
[HTML][HTML] Predicting exact valence and arousal values from EEG
F Galvão, SM Alarcão, MJ Fonseca - Sensors, 2021 - mdpi.com
Recognition of emotions from physiological signals, and in particular from
electroencephalography (EEG), is a field within affective computing gaining increasing …
electroencephalography (EEG), is a field within affective computing gaining increasing …
[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 …
An end-to-end visual-audio attention network for emotion recognition in user-generated videos
Emotion recognition in user-generated videos plays an important role in human-centered
computing. Existing methods mainly employ traditional two-stage shallow pipeline, ie …
computing. Existing methods mainly employ traditional two-stage shallow pipeline, ie …
A novel ensemble learning method using multiple objective particle swarm optimization for subject-independent EEG-based emotion recognition
R Li, C Ren, X Zhang, B Hu - Computers in biology and medicine, 2022 - Elsevier
Emotion recognition is a vital but challenging step in creating passive brain-computer
interface applications. In recent years, many studies on electroencephalogram (EEG)-based …
interface applications. In recent years, many studies on electroencephalogram (EEG)-based …
EEG based emotion recognition using fusion feature extraction method
Q Gao, C Wang, Z Wang, X Song, E Dong… - Multimedia Tools and …, 2020 - Springer
As a high-level function of the human brain, emotion is the external manifestation of people's
psychological characteristics. The emotion has a great impact on people's personality and …
psychological characteristics. The emotion has a great impact on people's personality and …
A modified feature selection method based on metaheuristic algorithms for speech emotion recognition
Feature selection plays an important role to build a successful speech emotion recognition
system. In this paper, a feature selection approach which modifies the initial population …
system. In this paper, a feature selection approach which modifies the initial population …
[HTML][HTML] Cross-subject EEG emotion recognition with self-organized graph neural network
As a physiological process and high-level cognitive behavior, emotion is an important
subarea in neuroscience research. Emotion recognition across subjects based on brain …
subarea in neuroscience research. Emotion recognition across subjects based on brain …