The issue of electroencephalogram (EEG)-based emotion recognition has great academic
and practical significance. Currently, there are numerous research trying to address this …

Objective. The data scarcity problem in emotion recognition from electroencephalography
(EEG) leads to difficulty in building an affective model with high accuracy using machine …

Graph convolutional networks (GCNs) have shown great prowess in learning topological
relationships among electroencephalogram (EEG) channels for EEG-based emotion …

Brain-computer interface (BCI) is oriented toward intuitive systems that users can easily
operate. Imagined speech and visual imagery are emerging paradigms that can directly …

Emotion is considered to be critical for the actual interpretation of actions and relationships.
Recognizing emotions from EEG signals is also becoming an important computer-aided …

Emotion recognition from electroencephalography (EEG) signals is a very cost-effective
method to monitor the general well-being of an individual, an employee of an organization …

This work proposes a novel multivariate-multiscale approach for computing the spectral and
temporal entropies from the multichannel electroencephalogram (EEG) signal. This …

Recently, the combination of neural network and attention mechanism is widely employed
for electroencephalogram (EEG) emotion recognition (EER) and has achieved remarkable …

Abstract Brain-Computer Interaction (BCI) system intelligence has become more dependent
on electroencephalogram (EEG)-based emotion recognition because of the numerous …

Emotion recognition from EEG signals has attracted much attention in affective computing.
Recently, a novel dynamic graph convolutional neural network (DGCNN) model was …