In recent years, electroencephalography (EEG) emotion recognition has achieved excellent
progress. However, the applied shallow convolutional neural networks (CNNs) cannot …

Deep learning techniques have drawn considerable interest in emotion recognition due to
recent technological developments in healthcare analytics. Automatic patient emotion …

Recently, multimodal fusion efforts have achieved remarkable success in Multimodal
Sentiment Analysis (MSA). However, most of the existing methods are based on model-level …

Electroencephalography (EEG)-based affective computing aims to recognize the emotional
state, which is the core technology of affective brain-computer interface (aBCI). This concept …

Affect recognition in a real-world, less constrained environment is the principal prerequisite
of the industrial-level usefulness of this technology. Monitoring the psychological profile …

The gated recurrent unit (GRU) network can effectively capture temporal information for 1D
signals, such as electroencephalography and event-related brain potential, and it has been …

Detecting emotions is a growing field aiming to comprehend and interpret human emotions
from various data sources, including text, voice, and physiological signals …

Graph neural networks (GNNs) have demonstrated efficient processing of graph-structured
data, making them a promising method for electroencephalogram (EEG) emotion …

Due to severe cross-subject data variations in electroencephalogram (EEG) signals, the
issue of subject-independent EEG-based emotion recognition remains challenging till today …

Automatic emotion recognition has attracted significant interest in healthcare, thanks to
remarkable developments made recently in smart and innovative technologies. A real-time …